| Title: | 'Amazon Web Services' Machine Learning Services |
|---|---|
| Description: | Interface to 'Amazon Web Services' machine learning services, including 'SageMaker' managed machine learning service, natural language processing, speech recognition, translation, and more <https://aws.amazon.com/machine-learning/>. |
| Authors: | David Kretch [aut], Adam Banker [aut], Dyfan Jones [cre], Amazon.com, Inc. [cph] |
| Maintainer: | Dyfan Jones <[email protected]> |
| License: | Apache License (>= 2.0) |
| Version: | 0.7.0 |
| Built: | 2024-09-12 18:36:49 UTC |
| Source: | CRAN |
Amazon Augmented AI (Amazon A2I) adds the benefit of human judgment to any machine learning application. When an AI application can't evaluate data with a high degree of confidence, human reviewers can take over. This human review is called a human review workflow. To create and start a human review workflow, you need three resources: a worker task template, a flow definition, and a human loop.
For information about these resources and prerequisites for using Amazon A2I, see Get Started with Amazon Augmented AI in the Amazon SageMaker Developer Guide.
This API reference includes information about API actions and data types that you can use to interact with Amazon A2I programmatically. Use this guide to:
Start a human loop with the
start_human_loop operation
when using Amazon A2I with a custom task type. To learn more about
the difference between custom and built-in task types, see Use Task Types
. To learn how to start a human loop using this API, see Create and Start a Human Loop for a Custom Task Type
in the Amazon SageMaker Developer Guide.
Manage your human loops. You can list all human loops that you have created, describe individual human loops, and stop and delete human loops. To learn more, see Monitor and Manage Your Human Loop in the Amazon SageMaker Developer Guide.
Amazon A2I integrates APIs from various AWS services to create and start human review workflows for those services. To learn how Amazon A2I uses these APIs, see Use APIs in Amazon A2I in the Amazon SageMaker Developer Guide.
augmentedairuntime( config = list(), credentials = list(), endpoint = NULL, region = NULL )augmentedairuntime( config = list(), credentials = list(), endpoint = NULL, region = NULL )
config |
Optional configuration of credentials, endpoint, and/or region.
|
credentials |
Optional credentials shorthand for the config parameter
|
endpoint |
Optional shorthand for complete URL to use for the constructed client. |
region |
Optional shorthand for AWS Region used in instantiating the client. |
A client for the service. You can call the service's operations using
syntax like svc$operation(...), where svc is the name you've assigned
to the client. The available operations are listed in the
Operations section.
svc <- augmentedairuntime(
config = list(
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string",
close_connection = "logical",
timeout = "numeric",
s3_force_path_style = "logical",
sts_regional_endpoint = "string"
),
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string"
)
| delete_human_loop | Deletes the specified human loop for a flow definition |
| describe_human_loop | Returns information about the specified human loop |
| list_human_loops | Returns information about human loops, given the specified parameters |
| start_human_loop | Starts a human loop, provided that at least one activation condition is met |
| stop_human_loop | Stops the specified human loop |
## Not run: svc <- augmentedairuntime() svc$delete_human_loop( Foo = 123 ) ## End(Not run)## Not run: svc <- augmentedairuntime() svc$delete_human_loop( Foo = 123 ) ## End(Not run)
Describes the API operations for creating, managing, fine-turning, and evaluating Amazon Bedrock models.
bedrock(config = list(), credentials = list(), endpoint = NULL, region = NULL)bedrock(config = list(), credentials = list(), endpoint = NULL, region = NULL)
config |
Optional configuration of credentials, endpoint, and/or region.
|
credentials |
Optional credentials shorthand for the config parameter
|
endpoint |
Optional shorthand for complete URL to use for the constructed client. |
region |
Optional shorthand for AWS Region used in instantiating the client. |
A client for the service. You can call the service's operations using
syntax like svc$operation(...), where svc is the name you've assigned
to the client. The available operations are listed in the
Operations section.
svc <- bedrock(
config = list(
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string",
close_connection = "logical",
timeout = "numeric",
s3_force_path_style = "logical",
sts_regional_endpoint = "string"
),
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string"
)
| batch_delete_evaluation_job | Creates a batch deletion job |
| create_evaluation_job | API operation for creating and managing Amazon Bedrock automatic model evaluation jobs and model evaluation jobs that use human workers |
| create_guardrail | Creates a guardrail to block topics and to implement safeguards for your generative AI applications |
| create_guardrail_version | Creates a version of the guardrail |
| create_model_copy_job | Copies a model to another region so that it can be used there |
| create_model_customization_job | Creates a fine-tuning job to customize a base model |
| create_model_import_job | Creates a model import job to import model that you have customized in other environments, such as Amazon SageMaker |
| create_model_invocation_job | Creates a batch inference job to invoke a model on multiple prompts |
| create_provisioned_model_throughput | Creates dedicated throughput for a base or custom model with the model units and for the duration that you specify |
| delete_custom_model | Deletes a custom model that you created earlier |
| delete_guardrail | Deletes a guardrail |
| delete_imported_model | Deletes a custom model that you imported earlier |
| delete_model_invocation_logging_configuration | Delete the invocation logging |
| delete_provisioned_model_throughput | Deletes a Provisioned Throughput |
| get_custom_model | Get the properties associated with a Amazon Bedrock custom model that you have created |
| get_evaluation_job | Retrieves the properties associated with a model evaluation job, including the status of the job |
| get_foundation_model | Get details about a Amazon Bedrock foundation model |
| get_guardrail | Gets details about a guardrail |
| get_imported_model | Gets properties associated with a customized model you imported |
| get_inference_profile | Gets information about an inference profile |
| get_model_copy_job | Retrieves information about a model copy job |
| get_model_customization_job | Retrieves the properties associated with a model-customization job, including the status of the job |
| get_model_import_job | Retrieves the properties associated with import model job, including the status of the job |
| get_model_invocation_job | Gets details about a batch inference job |
| get_model_invocation_logging_configuration | Get the current configuration values for model invocation logging |
| get_provisioned_model_throughput | Returns details for a Provisioned Throughput |
| list_custom_models | Returns a list of the custom models that you have created with the CreateModelCustomizationJob operation |
| list_evaluation_jobs | Lists model evaluation jobs |
| list_foundation_models | Lists Amazon Bedrock foundation models that you can use |
| list_guardrails | Lists details about all the guardrails in an account |
| list_imported_models | Returns a list of models you've imported |
| list_inference_profiles | Returns a list of inference profiles that you can use |
| list_model_copy_jobs | Returns a list of model copy jobs that you have submitted |
| list_model_customization_jobs | Returns a list of model customization jobs that you have submitted |
| list_model_import_jobs | Returns a list of import jobs you've submitted |
| list_model_invocation_jobs | Lists all batch inference jobs in the account |
| list_provisioned_model_throughputs | Lists the Provisioned Throughputs in the account |
| list_tags_for_resource | List the tags associated with the specified resource |
| put_model_invocation_logging_configuration | Set the configuration values for model invocation logging |
| stop_evaluation_job | Stops an in progress model evaluation job |
| stop_model_customization_job | Stops an active model customization job |
| stop_model_invocation_job | Stops a batch inference job |
| tag_resource | Associate tags with a resource |
| untag_resource | Remove one or more tags from a resource |
| update_guardrail | Updates a guardrail with the values you specify |
| update_provisioned_model_throughput | Updates the name or associated model for a Provisioned Throughput |
## Not run: svc <- bedrock() svc$batch_delete_evaluation_job( Foo = 123 ) ## End(Not run)## Not run: svc <- bedrock() svc$batch_delete_evaluation_job( Foo = 123 ) ## End(Not run)
Describes the API operations for running inference using Amazon Bedrock models.
bedrockruntime( config = list(), credentials = list(), endpoint = NULL, region = NULL )bedrockruntime( config = list(), credentials = list(), endpoint = NULL, region = NULL )
config |
Optional configuration of credentials, endpoint, and/or region.
|
credentials |
Optional credentials shorthand for the config parameter
|
endpoint |
Optional shorthand for complete URL to use for the constructed client. |
region |
Optional shorthand for AWS Region used in instantiating the client. |
A client for the service. You can call the service's operations using
syntax like svc$operation(...), where svc is the name you've assigned
to the client. The available operations are listed in the
Operations section.
svc <- bedrockruntime(
config = list(
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string",
close_connection = "logical",
timeout = "numeric",
s3_force_path_style = "logical",
sts_regional_endpoint = "string"
),
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string"
)
| apply_guardrail | The action to apply a guardrail |
| converse | Sends messages to the specified Amazon Bedrock model |
| converse_stream | Sends messages to the specified Amazon Bedrock model and returns the response in a stream |
| invoke_model | Invokes the specified Amazon Bedrock model to run inference using the prompt and inference parameters provided in the request body |
| invoke_model_with_response_stream | Invoke the specified Amazon Bedrock model to run inference using the prompt and inference parameters provided in the request body |
## Not run: svc <- bedrockruntime() svc$apply_guardrail( Foo = 123 ) ## End(Not run)## Not run: svc <- bedrockruntime() svc$apply_guardrail( Foo = 123 ) ## End(Not run)
Amazon Comprehend is an Amazon Web Services service for gaining insight into the content of documents. Use these actions to determine the topics contained in your documents, the topics they discuss, the predominant sentiment expressed in them, the predominant language used, and more.
comprehend( config = list(), credentials = list(), endpoint = NULL, region = NULL )comprehend( config = list(), credentials = list(), endpoint = NULL, region = NULL )
config |
Optional configuration of credentials, endpoint, and/or region.
|
credentials |
Optional credentials shorthand for the config parameter
|
endpoint |
Optional shorthand for complete URL to use for the constructed client. |
region |
Optional shorthand for AWS Region used in instantiating the client. |
A client for the service. You can call the service's operations using
syntax like svc$operation(...), where svc is the name you've assigned
to the client. The available operations are listed in the
Operations section.
svc <- comprehend(
config = list(
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string",
close_connection = "logical",
timeout = "numeric",
s3_force_path_style = "logical",
sts_regional_endpoint = "string"
),
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string"
)
| batch_detect_dominant_language | Determines the dominant language of the input text for a batch of documents |
| batch_detect_entities | Inspects the text of a batch of documents for named entities and returns information about them |
| batch_detect_key_phrases | Detects the key noun phrases found in a batch of documents |
| batch_detect_sentiment | Inspects a batch of documents and returns an inference of the prevailing sentiment, POSITIVE, NEUTRAL, MIXED, or NEGATIVE, in each one |
| batch_detect_syntax | Inspects the text of a batch of documents for the syntax and part of speech of the words in the document and returns information about them |
| batch_detect_targeted_sentiment | Inspects a batch of documents and returns a sentiment analysis for each entity identified in the documents |
| classify_document | Creates a classification request to analyze a single document in real-time |
| contains_pii_entities | Analyzes input text for the presence of personally identifiable information (PII) and returns the labels of identified PII entity types such as name, address, bank account number, or phone number |
| create_dataset | Creates a dataset to upload training or test data for a model associated with a flywheel |
| create_document_classifier | Creates a new document classifier that you can use to categorize documents |
| create_endpoint | Creates a model-specific endpoint for synchronous inference for a previously trained custom model For information about endpoints, see Managing endpoints |
| create_entity_recognizer | Creates an entity recognizer using submitted files |
| create_flywheel | A flywheel is an Amazon Web Services resource that orchestrates the ongoing training of a model for custom classification or custom entity recognition |
| delete_document_classifier | Deletes a previously created document classifier |
| delete_endpoint | Deletes a model-specific endpoint for a previously-trained custom model |
| delete_entity_recognizer | Deletes an entity recognizer |
| delete_flywheel | Deletes a flywheel |
| delete_resource_policy | Deletes a resource-based policy that is attached to a custom model |
| describe_dataset | Returns information about the dataset that you specify |
| describe_document_classification_job | Gets the properties associated with a document classification job |
| describe_document_classifier | Gets the properties associated with a document classifier |
| describe_dominant_language_detection_job | Gets the properties associated with a dominant language detection job |
| describe_endpoint | Gets the properties associated with a specific endpoint |
| describe_entities_detection_job | Gets the properties associated with an entities detection job |
| describe_entity_recognizer | Provides details about an entity recognizer including status, S3 buckets containing training data, recognizer metadata, metrics, and so on |
| describe_events_detection_job | Gets the status and details of an events detection job |
| describe_flywheel | Provides configuration information about the flywheel |
| describe_flywheel_iteration | Retrieve the configuration properties of a flywheel iteration |
| describe_key_phrases_detection_job | Gets the properties associated with a key phrases detection job |
| describe_pii_entities_detection_job | Gets the properties associated with a PII entities detection job |
| describe_resource_policy | Gets the details of a resource-based policy that is attached to a custom model, including the JSON body of the policy |
| describe_sentiment_detection_job | Gets the properties associated with a sentiment detection job |
| describe_targeted_sentiment_detection_job | Gets the properties associated with a targeted sentiment detection job |
| describe_topics_detection_job | Gets the properties associated with a topic detection job |
| detect_dominant_language | Determines the dominant language of the input text |
| detect_entities | Detects named entities in input text when you use the pre-trained model |
| detect_key_phrases | Detects the key noun phrases found in the text |
| detect_pii_entities | Inspects the input text for entities that contain personally identifiable information (PII) and returns information about them |
| detect_sentiment | Inspects text and returns an inference of the prevailing sentiment (POSITIVE, NEUTRAL, MIXED, or NEGATIVE) |
| detect_syntax | Inspects text for syntax and the part of speech of words in the document |
| detect_targeted_sentiment | Inspects the input text and returns a sentiment analysis for each entity identified in the text |
| detect_toxic_content | Performs toxicity analysis on the list of text strings that you provide as input |
| import_model | Creates a new custom model that replicates a source custom model that you import |
| list_datasets | List the datasets that you have configured in this Region |
| list_document_classification_jobs | Gets a list of the documentation classification jobs that you have submitted |
| list_document_classifiers | Gets a list of the document classifiers that you have created |
| list_document_classifier_summaries | Gets a list of summaries of the document classifiers that you have created |
| list_dominant_language_detection_jobs | Gets a list of the dominant language detection jobs that you have submitted |
| list_endpoints | Gets a list of all existing endpoints that you've created |
| list_entities_detection_jobs | Gets a list of the entity detection jobs that you have submitted |
| list_entity_recognizers | Gets a list of the properties of all entity recognizers that you created, including recognizers currently in training |
| list_entity_recognizer_summaries | Gets a list of summaries for the entity recognizers that you have created |
| list_events_detection_jobs | Gets a list of the events detection jobs that you have submitted |
| list_flywheel_iteration_history | Information about the history of a flywheel iteration |
| list_flywheels | Gets a list of the flywheels that you have created |
| list_key_phrases_detection_jobs | Get a list of key phrase detection jobs that you have submitted |
| list_pii_entities_detection_jobs | Gets a list of the PII entity detection jobs that you have submitted |
| list_sentiment_detection_jobs | Gets a list of sentiment detection jobs that you have submitted |
| list_tags_for_resource | Lists all tags associated with a given Amazon Comprehend resource |
| list_targeted_sentiment_detection_jobs | Gets a list of targeted sentiment detection jobs that you have submitted |
| list_topics_detection_jobs | Gets a list of the topic detection jobs that you have submitted |
| put_resource_policy | Attaches a resource-based policy to a custom model |
| start_document_classification_job | Starts an asynchronous document classification job using a custom classification model |
| start_dominant_language_detection_job | Starts an asynchronous dominant language detection job for a collection of documents |
| start_entities_detection_job | Starts an asynchronous entity detection job for a collection of documents |
| start_events_detection_job | Starts an asynchronous event detection job for a collection of documents |
| start_flywheel_iteration | Start the flywheel iteration |
| start_key_phrases_detection_job | Starts an asynchronous key phrase detection job for a collection of documents |
| start_pii_entities_detection_job | Starts an asynchronous PII entity detection job for a collection of documents |
| start_sentiment_detection_job | Starts an asynchronous sentiment detection job for a collection of documents |
| start_targeted_sentiment_detection_job | Starts an asynchronous targeted sentiment detection job for a collection of documents |
| start_topics_detection_job | Starts an asynchronous topic detection job |
| stop_dominant_language_detection_job | Stops a dominant language detection job in progress |
| stop_entities_detection_job | Stops an entities detection job in progress |
| stop_events_detection_job | Stops an events detection job in progress |
| stop_key_phrases_detection_job | Stops a key phrases detection job in progress |
| stop_pii_entities_detection_job | Stops a PII entities detection job in progress |
| stop_sentiment_detection_job | Stops a sentiment detection job in progress |
| stop_targeted_sentiment_detection_job | Stops a targeted sentiment detection job in progress |
| stop_training_document_classifier | Stops a document classifier training job while in progress |
| stop_training_entity_recognizer | Stops an entity recognizer training job while in progress |
| tag_resource | Associates a specific tag with an Amazon Comprehend resource |
| untag_resource | Removes a specific tag associated with an Amazon Comprehend resource |
| update_endpoint | Updates information about the specified endpoint |
| update_flywheel | Update the configuration information for an existing flywheel |
## Not run: svc <- comprehend() svc$batch_detect_dominant_language( Foo = 123 ) ## End(Not run)## Not run: svc <- comprehend() svc$batch_detect_dominant_language( Foo = 123 ) ## End(Not run)
Amazon Comprehend Medical extracts structured information from unstructured clinical text. Use these actions to gain insight in your documents. Amazon Comprehend Medical only detects entities in English language texts. Amazon Comprehend Medical places limits on the sizes of files allowed for different API operations. To learn more, see Guidelines and quotas in the Amazon Comprehend Medical Developer Guide.
comprehendmedical( config = list(), credentials = list(), endpoint = NULL, region = NULL )comprehendmedical( config = list(), credentials = list(), endpoint = NULL, region = NULL )
config |
Optional configuration of credentials, endpoint, and/or region.
|
credentials |
Optional credentials shorthand for the config parameter
|
endpoint |
Optional shorthand for complete URL to use for the constructed client. |
region |
Optional shorthand for AWS Region used in instantiating the client. |
A client for the service. You can call the service's operations using
syntax like svc$operation(...), where svc is the name you've assigned
to the client. The available operations are listed in the
Operations section.
svc <- comprehendmedical(
config = list(
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string",
close_connection = "logical",
timeout = "numeric",
s3_force_path_style = "logical",
sts_regional_endpoint = "string"
),
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string"
)
| describe_entities_detection_v2_job | Gets the properties associated with a medical entities detection job |
| describe_icd10cm_inference_job | Gets the properties associated with an InferICD10CM job |
| describe_phi_detection_job | Gets the properties associated with a protected health information (PHI) detection job |
| describe_rx_norm_inference_job | Gets the properties associated with an InferRxNorm job |
| describe_snomedct_inference_job | Gets the properties associated with an InferSNOMEDCT job |
| detect_entities | The DetectEntities operation is deprecated |
| detect_entities_v2 | Inspects the clinical text for a variety of medical entities and returns specific information about them such as entity category, location, and confidence score on that information |
| detect_phi | Inspects the clinical text for protected health information (PHI) entities and returns the entity category, location, and confidence score for each entity |
| infer_icd10cm | InferICD10CM detects medical conditions as entities listed in a patient record and links those entities to normalized concept identifiers in the ICD-10-CM knowledge base from the Centers for Disease Control |
| infer_rx_norm | InferRxNorm detects medications as entities listed in a patient record and links to the normalized concept identifiers in the RxNorm database from the National Library of Medicine |
| infer_snomedct | InferSNOMEDCT detects possible medical concepts as entities and links them to codes from the Systematized Nomenclature of Medicine, Clinical Terms (SNOMED-CT) ontology |
| list_entities_detection_v2_jobs | Gets a list of medical entity detection jobs that you have submitted |
| list_icd10cm_inference_jobs | Gets a list of InferICD10CM jobs that you have submitted |
| list_phi_detection_jobs | Gets a list of protected health information (PHI) detection jobs you have submitted |
| list_rx_norm_inference_jobs | Gets a list of InferRxNorm jobs that you have submitted |
| list_snomedct_inference_jobs | Gets a list of InferSNOMEDCT jobs a user has submitted |
| start_entities_detection_v2_job | Starts an asynchronous medical entity detection job for a collection of documents |
| start_icd10cm_inference_job | Starts an asynchronous job to detect medical conditions and link them to the ICD-10-CM ontology |
| start_phi_detection_job | Starts an asynchronous job to detect protected health information (PHI) |
| start_rx_norm_inference_job | Starts an asynchronous job to detect medication entities and link them to the RxNorm ontology |
| start_snomedct_inference_job | Starts an asynchronous job to detect medical concepts and link them to the SNOMED-CT ontology |
| stop_entities_detection_v2_job | Stops a medical entities detection job in progress |
| stop_icd10cm_inference_job | Stops an InferICD10CM inference job in progress |
| stop_phi_detection_job | Stops a protected health information (PHI) detection job in progress |
| stop_rx_norm_inference_job | Stops an InferRxNorm inference job in progress |
| stop_snomedct_inference_job | Stops an InferSNOMEDCT inference job in progress |
## Not run: svc <- comprehendmedical() svc$describe_entities_detection_v2_job( Foo = 123 ) ## End(Not run)## Not run: svc <- comprehendmedical() svc$describe_entities_detection_v2_job( Foo = 123 ) ## End(Not run)
Elastic Inference public APIs.
February 15, 2023: Starting April 15, 2023, AWS will not onboard new customers to Amazon Elastic Inference (EI), and will help current customers migrate their workloads to options that offer better price and performance. After April 15, 2023, new customers will not be able to launch instances with Amazon EI accelerators in Amazon SageMaker, Amazon ECS, or Amazon EC2. However, customers who have used Amazon EI at least once during the past 30-day period are considered current customers and will be able to continue using the service.
elasticinference( config = list(), credentials = list(), endpoint = NULL, region = NULL )elasticinference( config = list(), credentials = list(), endpoint = NULL, region = NULL )
config |
Optional configuration of credentials, endpoint, and/or region.
|
credentials |
Optional credentials shorthand for the config parameter
|
endpoint |
Optional shorthand for complete URL to use for the constructed client. |
region |
Optional shorthand for AWS Region used in instantiating the client. |
A client for the service. You can call the service's operations using
syntax like svc$operation(...), where svc is the name you've assigned
to the client. The available operations are listed in the
Operations section.
svc <- elasticinference(
config = list(
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string",
close_connection = "logical",
timeout = "numeric",
s3_force_path_style = "logical",
sts_regional_endpoint = "string"
),
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string"
)
| describe_accelerator_offerings | Describes the locations in which a given accelerator type or set of types is present in a given region |
| describe_accelerators | Describes information over a provided set of accelerators belonging to an account |
| describe_accelerator_types | Describes the accelerator types available in a given region, as well as their characteristics, such as memory and throughput |
| list_tags_for_resource | Returns all tags of an Elastic Inference Accelerator |
| tag_resource | Adds the specified tags to an Elastic Inference Accelerator |
| untag_resource | Removes the specified tags from an Elastic Inference Accelerator |
## Not run: svc <- elasticinference() svc$describe_accelerator_offerings( Foo = 123 ) ## End(Not run)## Not run: svc <- elasticinference() svc$describe_accelerator_offerings( Foo = 123 ) ## End(Not run)
Provides APIs for creating and managing Amazon Forecast resources.
forecastqueryservice( config = list(), credentials = list(), endpoint = NULL, region = NULL )forecastqueryservice( config = list(), credentials = list(), endpoint = NULL, region = NULL )
config |
Optional configuration of credentials, endpoint, and/or region.
|
credentials |
Optional credentials shorthand for the config parameter
|
endpoint |
Optional shorthand for complete URL to use for the constructed client. |
region |
Optional shorthand for AWS Region used in instantiating the client. |
A client for the service. You can call the service's operations using
syntax like svc$operation(...), where svc is the name you've assigned
to the client. The available operations are listed in the
Operations section.
svc <- forecastqueryservice(
config = list(
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string",
close_connection = "logical",
timeout = "numeric",
s3_force_path_style = "logical",
sts_regional_endpoint = "string"
),
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string"
)
| query_forecast | Retrieves a forecast for a single item, filtered by the supplied criteria |
| query_what_if_forecast | Retrieves a what-if forecast |
## Not run: svc <- forecastqueryservice() svc$query_forecast( Foo = 123 ) ## End(Not run)## Not run: svc <- forecastqueryservice() svc$query_forecast( Foo = 123 ) ## End(Not run)
Provides APIs for creating and managing Amazon Forecast resources.
forecastservice( config = list(), credentials = list(), endpoint = NULL, region = NULL )forecastservice( config = list(), credentials = list(), endpoint = NULL, region = NULL )
config |
Optional configuration of credentials, endpoint, and/or region.
|
credentials |
Optional credentials shorthand for the config parameter
|
endpoint |
Optional shorthand for complete URL to use for the constructed client. |
region |
Optional shorthand for AWS Region used in instantiating the client. |
A client for the service. You can call the service's operations using
syntax like svc$operation(...), where svc is the name you've assigned
to the client. The available operations are listed in the
Operations section.
svc <- forecastservice(
config = list(
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string",
close_connection = "logical",
timeout = "numeric",
s3_force_path_style = "logical",
sts_regional_endpoint = "string"
),
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string"
)
| create_auto_predictor | Creates an Amazon Forecast predictor |
| create_dataset | Creates an Amazon Forecast dataset |
| create_dataset_group | Creates a dataset group, which holds a collection of related datasets |
| create_dataset_import_job | Imports your training data to an Amazon Forecast dataset |
| create_explainability | Explainability is only available for Forecasts and Predictors generated from an AutoPredictor (CreateAutoPredictor) |
| create_explainability_export | Exports an Explainability resource created by the CreateExplainability operation |
| create_forecast | Creates a forecast for each item in the TARGET_TIME_SERIES dataset that was used to train the predictor |
| create_forecast_export_job | Exports a forecast created by the CreateForecast operation to your Amazon Simple Storage Service (Amazon S3) bucket |
| create_monitor | Creates a predictor monitor resource for an existing auto predictor |
| create_predictor | This operation creates a legacy predictor that does not include all the predictor functionalities provided by Amazon Forecast |
| create_predictor_backtest_export_job | Exports backtest forecasts and accuracy metrics generated by the CreateAutoPredictor or CreatePredictor operations |
| create_what_if_analysis | What-if analysis is a scenario modeling technique where you make a hypothetical change to a time series and compare the forecasts generated by these changes against the baseline, unchanged time series |
| create_what_if_forecast | A what-if forecast is a forecast that is created from a modified version of the baseline forecast |
| create_what_if_forecast_export | Exports a forecast created by the CreateWhatIfForecast operation to your Amazon Simple Storage Service (Amazon S3) bucket |
| delete_dataset | Deletes an Amazon Forecast dataset that was created using the CreateDataset operation |
| delete_dataset_group | Deletes a dataset group created using the CreateDatasetGroup operation |
| delete_dataset_import_job | Deletes a dataset import job created using the CreateDatasetImportJob operation |
| delete_explainability | Deletes an Explainability resource |
| delete_explainability_export | Deletes an Explainability export |
| delete_forecast | Deletes a forecast created using the CreateForecast operation |
| delete_forecast_export_job | Deletes a forecast export job created using the CreateForecastExportJob operation |
| delete_monitor | Deletes a monitor resource |
| delete_predictor | Deletes a predictor created using the DescribePredictor or CreatePredictor operations |
| delete_predictor_backtest_export_job | Deletes a predictor backtest export job |
| delete_resource_tree | Deletes an entire resource tree |
| delete_what_if_analysis | Deletes a what-if analysis created using the CreateWhatIfAnalysis operation |
| delete_what_if_forecast | Deletes a what-if forecast created using the CreateWhatIfForecast operation |
| delete_what_if_forecast_export | Deletes a what-if forecast export created using the CreateWhatIfForecastExport operation |
| describe_auto_predictor | Describes a predictor created using the CreateAutoPredictor operation |
| describe_dataset | Describes an Amazon Forecast dataset created using the CreateDataset operation |
| describe_dataset_group | Describes a dataset group created using the CreateDatasetGroup operation |
| describe_dataset_import_job | Describes a dataset import job created using the CreateDatasetImportJob operation |
| describe_explainability | Describes an Explainability resource created using the CreateExplainability operation |
| describe_explainability_export | Describes an Explainability export created using the CreateExplainabilityExport operation |
| describe_forecast | Describes a forecast created using the CreateForecast operation |
| describe_forecast_export_job | Describes a forecast export job created using the CreateForecastExportJob operation |
| describe_monitor | Describes a monitor resource |
| describe_predictor | This operation is only valid for legacy predictors created with CreatePredictor |
| describe_predictor_backtest_export_job | Describes a predictor backtest export job created using the CreatePredictorBacktestExportJob operation |
| describe_what_if_analysis | Describes the what-if analysis created using the CreateWhatIfAnalysis operation |
| describe_what_if_forecast | Describes the what-if forecast created using the CreateWhatIfForecast operation |
| describe_what_if_forecast_export | Describes the what-if forecast export created using the CreateWhatIfForecastExport operation |
| get_accuracy_metrics | Provides metrics on the accuracy of the models that were trained by the CreatePredictor operation |
| list_dataset_groups | Returns a list of dataset groups created using the CreateDatasetGroup operation |
| list_dataset_import_jobs | Returns a list of dataset import jobs created using the CreateDatasetImportJob operation |
| list_datasets | Returns a list of datasets created using the CreateDataset operation |
| list_explainabilities | Returns a list of Explainability resources created using the CreateExplainability operation |
| list_explainability_exports | Returns a list of Explainability exports created using the CreateExplainabilityExport operation |
| list_forecast_export_jobs | Returns a list of forecast export jobs created using the CreateForecastExportJob operation |
| list_forecasts | Returns a list of forecasts created using the CreateForecast operation |
| list_monitor_evaluations | Returns a list of the monitoring evaluation results and predictor events collected by the monitor resource during different windows of time |
| list_monitors | Returns a list of monitors created with the CreateMonitor operation and CreateAutoPredictor operation |
| list_predictor_backtest_export_jobs | Returns a list of predictor backtest export jobs created using the CreatePredictorBacktestExportJob operation |
| list_predictors | Returns a list of predictors created using the CreateAutoPredictor or CreatePredictor operations |
| list_tags_for_resource | Lists the tags for an Amazon Forecast resource |
| list_what_if_analyses | Returns a list of what-if analyses created using the CreateWhatIfAnalysis operation |
| list_what_if_forecast_exports | Returns a list of what-if forecast exports created using the CreateWhatIfForecastExport operation |
| list_what_if_forecasts | Returns a list of what-if forecasts created using the CreateWhatIfForecast operation |
| resume_resource | Resumes a stopped monitor resource |
| stop_resource | Stops a resource |
| tag_resource | Associates the specified tags to a resource with the specified resourceArn |
| untag_resource | Deletes the specified tags from a resource |
| update_dataset_group | Replaces the datasets in a dataset group with the specified datasets |
## Not run: svc <- forecastservice() svc$create_auto_predictor( Foo = 123 ) ## End(Not run)## Not run: svc <- forecastservice() svc$create_auto_predictor( Foo = 123 ) ## End(Not run)
This is the Amazon Fraud Detector API Reference. This guide is for developers who need detailed information about Amazon Fraud Detector API actions, data types, and errors. For more information about Amazon Fraud Detector features, see the Amazon Fraud Detector User Guide.
We provide the Query API as well as AWS software development kits (SDK) for Amazon Fraud Detector in Java and Python programming languages.
The Amazon Fraud Detector Query API provides HTTPS requests that use the
HTTP verb GET or POST and a Query parameter Action. AWS SDK provides
libraries, sample code, tutorials, and other resources for software
developers who prefer to build applications using language-specific APIs
instead of submitting a request over HTTP or HTTPS. These libraries
provide basic functions that automatically take care of tasks such as
cryptographically signing your requests, retrying requests, and handling
error responses, so that it is easier for you to get started. For more
information about the AWS SDKs, go to Tools to build on AWS page, scroll down to the
SDK section, and choose plus (+) sign to expand the section.
frauddetector( config = list(), credentials = list(), endpoint = NULL, region = NULL )frauddetector( config = list(), credentials = list(), endpoint = NULL, region = NULL )
config |
Optional configuration of credentials, endpoint, and/or region.
|
credentials |
Optional credentials shorthand for the config parameter
|
endpoint |
Optional shorthand for complete URL to use for the constructed client. |
region |
Optional shorthand for AWS Region used in instantiating the client. |
A client for the service. You can call the service's operations using
syntax like svc$operation(...), where svc is the name you've assigned
to the client. The available operations are listed in the
Operations section.
svc <- frauddetector(
config = list(
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string",
close_connection = "logical",
timeout = "numeric",
s3_force_path_style = "logical",
sts_regional_endpoint = "string"
),
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string"
)
| batch_create_variable | Creates a batch of variables |
| batch_get_variable | Gets a batch of variables |
| cancel_batch_import_job | Cancels an in-progress batch import job |
| cancel_batch_prediction_job | Cancels the specified batch prediction job |
| create_batch_import_job | Creates a batch import job |
| create_batch_prediction_job | Creates a batch prediction job |
| create_detector_version | Creates a detector version |
| create_list | Creates a list |
| create_model | Creates a model using the specified model type |
| create_model_version | Creates a version of the model using the specified model type and model id |
| create_rule | Creates a rule for use with the specified detector |
| create_variable | Creates a variable |
| delete_batch_import_job | Deletes the specified batch import job ID record |
| delete_batch_prediction_job | Deletes a batch prediction job |
| delete_detector | Deletes the detector |
| delete_detector_version | Deletes the detector version |
| delete_entity_type | Deletes an entity type |
| delete_event | Deletes the specified event |
| delete_events_by_event_type | Deletes all events of a particular event type |
| delete_event_type | Deletes an event type |
| delete_external_model | Removes a SageMaker model from Amazon Fraud Detector |
| delete_label | Deletes a label |
| delete_list | Deletes the list, provided it is not used in a rule |
| delete_model | Deletes a model |
| delete_model_version | Deletes a model version |
| delete_outcome | Deletes an outcome |
| delete_rule | Deletes the rule |
| delete_variable | Deletes a variable |
| describe_detector | Gets all versions for a specified detector |
| describe_model_versions | Gets all of the model versions for the specified model type or for the specified model type and model ID |
| get_batch_import_jobs | Gets all batch import jobs or a specific job of the specified ID |
| get_batch_prediction_jobs | Gets all batch prediction jobs or a specific job if you specify a job ID |
| get_delete_events_by_event_type_status | Retrieves the status of a DeleteEventsByEventType action |
| get_detectors | Gets all detectors or a single detector if a detectorId is specified |
| get_detector_version | Gets a particular detector version |
| get_entity_types | Gets all entity types or a specific entity type if a name is specified |
| get_event | Retrieves details of events stored with Amazon Fraud Detector |
| get_event_prediction | Evaluates an event against a detector version |
| get_event_prediction_metadata | Gets details of the past fraud predictions for the specified event ID, event type, detector ID, and detector version ID that was generated in the specified time period |
| get_event_types | Gets all event types or a specific event type if name is provided |
| get_external_models | Gets the details for one or more Amazon SageMaker models that have been imported into the service |
| get_kms_encryption_key | Gets the encryption key if a KMS key has been specified to be used to encrypt content in Amazon Fraud Detector |
| get_labels | Gets all labels or a specific label if name is provided |
| get_list_elements | Gets all the elements in the specified list |
| get_lists_metadata | Gets the metadata of either all the lists under the account or the specified list |
| get_models | Gets one or more models |
| get_model_version | Gets the details of the specified model version |
| get_outcomes | Gets one or more outcomes |
| get_rules | Get all rules for a detector (paginated) if ruleId and ruleVersion are not specified |
| get_variables | Gets all of the variables or the specific variable |
| list_event_predictions | Gets a list of past predictions |
| list_tags_for_resource | Lists all tags associated with the resource |
| put_detector | Creates or updates a detector |
| put_entity_type | Creates or updates an entity type |
| put_event_type | Creates or updates an event type |
| put_external_model | Creates or updates an Amazon SageMaker model endpoint |
| put_kms_encryption_key | Specifies the KMS key to be used to encrypt content in Amazon Fraud Detector |
| put_label | Creates or updates label |
| put_outcome | Creates or updates an outcome |
| send_event | Stores events in Amazon Fraud Detector without generating fraud predictions for those events |
| tag_resource | Assigns tags to a resource |
| untag_resource | Removes tags from a resource |
| update_detector_version | Updates a detector version |
| update_detector_version_metadata | Updates the detector version's description |
| update_detector_version_status | Updates the detector version’s status |
| update_event_label | Updates the specified event with a new label |
| update_list | Updates a list |
| update_model | Updates model description |
| update_model_version | Updates a model version |
| update_model_version_status | Updates the status of a model version |
| update_rule_metadata | Updates a rule's metadata |
| update_rule_version | Updates a rule version resulting in a new rule version |
| update_variable | Updates a variable |
## Not run: svc <- frauddetector() svc$batch_create_variable( Foo = 123 ) ## End(Not run)## Not run: svc <- frauddetector() svc$batch_create_variable( Foo = 123 ) ## End(Not run)
Amazon Lex Build-Time Actions
Amazon Lex is an AWS service for building conversational voice and text interfaces. Use these actions to create, update, and delete conversational bots for new and existing client applications.
lexmodelbuildingservice( config = list(), credentials = list(), endpoint = NULL, region = NULL )lexmodelbuildingservice( config = list(), credentials = list(), endpoint = NULL, region = NULL )
config |
Optional configuration of credentials, endpoint, and/or region.
|
credentials |
Optional credentials shorthand for the config parameter
|
endpoint |
Optional shorthand for complete URL to use for the constructed client. |
region |
Optional shorthand for AWS Region used in instantiating the client. |
A client for the service. You can call the service's operations using
syntax like svc$operation(...), where svc is the name you've assigned
to the client. The available operations are listed in the
Operations section.
svc <- lexmodelbuildingservice(
config = list(
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string",
close_connection = "logical",
timeout = "numeric",
s3_force_path_style = "logical",
sts_regional_endpoint = "string"
),
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string"
)
| create_bot_version | Creates a new version of the bot based on the $LATEST version |
| create_intent_version | Creates a new version of an intent based on the $LATEST version of the intent |
| create_slot_type_version | Creates a new version of a slot type based on the $LATEST version of the specified slot type |
| delete_bot | Deletes all versions of the bot, including the $LATEST version |
| delete_bot_alias | Deletes an alias for the specified bot |
| delete_bot_channel_association | Deletes the association between an Amazon Lex bot and a messaging platform |
| delete_bot_version | Deletes a specific version of a bot |
| delete_intent | Deletes all versions of the intent, including the $LATEST version |
| delete_intent_version | Deletes a specific version of an intent |
| delete_slot_type | Deletes all versions of the slot type, including the $LATEST version |
| delete_slot_type_version | Deletes a specific version of a slot type |
| delete_utterances | Deletes stored utterances |
| get_bot | Returns metadata information for a specific bot |
| get_bot_alias | Returns information about an Amazon Lex bot alias |
| get_bot_aliases | Returns a list of aliases for a specified Amazon Lex bot |
| get_bot_channel_association | Returns information about the association between an Amazon Lex bot and a messaging platform |
| get_bot_channel_associations | Returns a list of all of the channels associated with the specified bot |
| get_bots | Returns bot information as follows: |
| get_bot_versions | Gets information about all of the versions of a bot |
| get_builtin_intent | Returns information about a built-in intent |
| get_builtin_intents | Gets a list of built-in intents that meet the specified criteria |
| get_builtin_slot_types | Gets a list of built-in slot types that meet the specified criteria |
| get_export | Exports the contents of a Amazon Lex resource in a specified format |
| get_import | Gets information about an import job started with the StartImport operation |
| get_intent | Returns information about an intent |
| get_intents | Returns intent information as follows: |
| get_intent_versions | Gets information about all of the versions of an intent |
| get_migration | Provides details about an ongoing or complete migration from an Amazon Lex V1 bot to an Amazon Lex V2 bot |
| get_migrations | Gets a list of migrations between Amazon Lex V1 and Amazon Lex V2 |
| get_slot_type | Returns information about a specific version of a slot type |
| get_slot_types | Returns slot type information as follows: |
| get_slot_type_versions | Gets information about all versions of a slot type |
| get_utterances_view | Use the GetUtterancesView operation to get information about the utterances that your users have made to your bot |
| list_tags_for_resource | Gets a list of tags associated with the specified resource |
| put_bot | Creates an Amazon Lex conversational bot or replaces an existing bot |
| put_bot_alias | Creates an alias for the specified version of the bot or replaces an alias for the specified bot |
| put_intent | Creates an intent or replaces an existing intent |
| put_slot_type | Creates a custom slot type or replaces an existing custom slot type |
| start_import | Starts a job to import a resource to Amazon Lex |
| start_migration | Starts migrating a bot from Amazon Lex V1 to Amazon Lex V2 |
| tag_resource | Adds the specified tags to the specified resource |
| untag_resource | Removes tags from a bot, bot alias or bot channel |
## Not run: svc <- lexmodelbuildingservice() # This example shows how to get configuration information for a bot. svc$get_bot( name = "DocOrderPizza", versionOrAlias = "$LATEST" ) ## End(Not run)## Not run: svc <- lexmodelbuildingservice() # This example shows how to get configuration information for a bot. svc$get_bot( name = "DocOrderPizza", versionOrAlias = "$LATEST" ) ## End(Not run)
Amazon Lex Model Building V2
lexmodelsv2( config = list(), credentials = list(), endpoint = NULL, region = NULL )lexmodelsv2( config = list(), credentials = list(), endpoint = NULL, region = NULL )
config |
Optional configuration of credentials, endpoint, and/or region.
|
credentials |
Optional credentials shorthand for the config parameter
|
endpoint |
Optional shorthand for complete URL to use for the constructed client. |
region |
Optional shorthand for AWS Region used in instantiating the client. |
A client for the service. You can call the service's operations using
syntax like svc$operation(...), where svc is the name you've assigned
to the client. The available operations are listed in the
Operations section.
svc <- lexmodelsv2(
config = list(
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string",
close_connection = "logical",
timeout = "numeric",
s3_force_path_style = "logical",
sts_regional_endpoint = "string"
),
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string"
)
| batch_create_custom_vocabulary_item | Create a batch of custom vocabulary items for a given bot locale's custom vocabulary |
| batch_delete_custom_vocabulary_item | Delete a batch of custom vocabulary items for a given bot locale's custom vocabulary |
| batch_update_custom_vocabulary_item | Update a batch of custom vocabulary items for a given bot locale's custom vocabulary |
| build_bot_locale | Builds a bot, its intents, and its slot types into a specific locale |
| create_bot | Creates an Amazon Lex conversational bot |
| create_bot_alias | Creates an alias for the specified version of a bot |
| create_bot_locale | Creates a locale in the bot |
| create_bot_replica | Action to create a replication of the source bot in the secondary region |
| create_bot_version | Creates an immutable version of the bot |
| create_export | Creates a zip archive containing the contents of a bot or a bot locale |
| create_intent | Creates an intent |
| create_resource_policy | Creates a new resource policy with the specified policy statements |
| create_resource_policy_statement | Adds a new resource policy statement to a bot or bot alias |
| create_slot | Creates a slot in an intent |
| create_slot_type | Creates a custom slot type |
| create_test_set_discrepancy_report | Create a report that describes the differences between the bot and the test set |
| create_upload_url | Gets a pre-signed S3 write URL that you use to upload the zip archive when importing a bot or a bot locale |
| delete_bot | Deletes all versions of a bot, including the Draft version |
| delete_bot_alias | Deletes the specified bot alias |
| delete_bot_locale | Removes a locale from a bot |
| delete_bot_replica | The action to delete the replicated bot in the secondary region |
| delete_bot_version | Deletes a specific version of a bot |
| delete_custom_vocabulary | Removes a custom vocabulary from the specified locale in the specified bot |
| delete_export | Removes a previous export and the associated files stored in an S3 bucket |
| delete_import | Removes a previous import and the associated file stored in an S3 bucket |
| delete_intent | Removes the specified intent |
| delete_resource_policy | Removes an existing policy from a bot or bot alias |
| delete_resource_policy_statement | Deletes a policy statement from a resource policy |
| delete_slot | Deletes the specified slot from an intent |
| delete_slot_type | Deletes a slot type from a bot locale |
| delete_test_set | The action to delete the selected test set |
| delete_utterances | Deletes stored utterances |
| describe_bot | Provides metadata information about a bot |
| describe_bot_alias | Get information about a specific bot alias |
| describe_bot_locale | Describes the settings that a bot has for a specific locale |
| describe_bot_recommendation | Provides metadata information about a bot recommendation |
| describe_bot_replica | Monitors the bot replication status through the UI console |
| describe_bot_resource_generation | Returns information about a request to generate a bot through natural language description, made through the StartBotResource API |
| describe_bot_version | Provides metadata about a version of a bot |
| describe_custom_vocabulary_metadata | Provides metadata information about a custom vocabulary |
| describe_export | Gets information about a specific export |
| describe_import | Gets information about a specific import |
| describe_intent | Returns metadata about an intent |
| describe_resource_policy | Gets the resource policy and policy revision for a bot or bot alias |
| describe_slot | Gets metadata information about a slot |
| describe_slot_type | Gets metadata information about a slot type |
| describe_test_execution | Gets metadata information about the test execution |
| describe_test_set | Gets metadata information about the test set |
| describe_test_set_discrepancy_report | Gets metadata information about the test set discrepancy report |
| describe_test_set_generation | Gets metadata information about the test set generation |
| generate_bot_element | Generates sample utterances for an intent |
| get_test_execution_artifacts_url | The pre-signed Amazon S3 URL to download the test execution result artifacts |
| list_aggregated_utterances | Provides a list of utterances that users have sent to the bot |
| list_bot_aliases | Gets a list of aliases for the specified bot |
| list_bot_alias_replicas | The action to list the replicated bots created from the source bot alias |
| list_bot_locales | Gets a list of locales for the specified bot |
| list_bot_recommendations | Get a list of bot recommendations that meet the specified criteria |
| list_bot_replicas | The action to list the replicated bots |
| list_bot_resource_generations | Lists the generation requests made for a bot locale |
| list_bots | Gets a list of available bots |
| list_bot_version_replicas | Contains information about all the versions replication statuses applicable for Global Resiliency |
| list_bot_versions | Gets information about all of the versions of a bot |
| list_built_in_intents | Gets a list of built-in intents provided by Amazon Lex that you can use in your bot |
| list_built_in_slot_types | Gets a list of built-in slot types that meet the specified criteria |
| list_custom_vocabulary_items | Paginated list of custom vocabulary items for a given bot locale's custom vocabulary |
| list_exports | Lists the exports for a bot, bot locale, or custom vocabulary |
| list_imports | Lists the imports for a bot, bot locale, or custom vocabulary |
| list_intent_metrics | Retrieves summary metrics for the intents in your bot |
| list_intent_paths | Retrieves summary statistics for a path of intents that users take over sessions with your bot |
| list_intents | Get a list of intents that meet the specified criteria |
| list_intent_stage_metrics | Retrieves summary metrics for the stages within intents in your bot |
| list_recommended_intents | Gets a list of recommended intents provided by the bot recommendation that you can use in your bot |
| list_session_analytics_data | Retrieves a list of metadata for individual user sessions with your bot |
| list_session_metrics | Retrieves summary metrics for the user sessions with your bot |
| list_slots | Gets a list of slots that match the specified criteria |
| list_slot_types | Gets a list of slot types that match the specified criteria |
| list_tags_for_resource | Gets a list of tags associated with a resource |
| list_test_execution_result_items | Gets a list of test execution result items |
| list_test_executions | The list of test set executions |
| list_test_set_records | The list of test set records |
| list_test_sets | The list of the test sets |
| list_utterance_analytics_data | To use this API operation, your IAM role must have permissions to perform the ListAggregatedUtterances operation, which provides access to utterance-related analytics |
| list_utterance_metrics | To use this API operation, your IAM role must have permissions to perform the ListAggregatedUtterances operation, which provides access to utterance-related analytics |
| search_associated_transcripts | Search for associated transcripts that meet the specified criteria |
| start_bot_recommendation | Use this to provide your transcript data, and to start the bot recommendation process |
| start_bot_resource_generation | Starts a request for the descriptive bot builder to generate a bot locale configuration based on the prompt you provide it |
| start_import | Starts importing a bot, bot locale, or custom vocabulary from a zip archive that you uploaded to an S3 bucket |
| start_test_execution | The action to start test set execution |
| start_test_set_generation | The action to start the generation of test set |
| stop_bot_recommendation | Stop an already running Bot Recommendation request |
| tag_resource | Adds the specified tags to the specified resource |
| untag_resource | Removes tags from a bot, bot alias, or bot channel |
| update_bot | Updates the configuration of an existing bot |
| update_bot_alias | Updates the configuration of an existing bot alias |
| update_bot_locale | Updates the settings that a bot has for a specific locale |
| update_bot_recommendation | Updates an existing bot recommendation request |
| update_export | Updates the password used to protect an export zip archive |
| update_intent | Updates the settings for an intent |
| update_resource_policy | Replaces the existing resource policy for a bot or bot alias with a new one |
| update_slot | Updates the settings for a slot |
| update_slot_type | Updates the configuration of an existing slot type |
| update_test_set | The action to update the test set |
## Not run: svc <- lexmodelsv2() svc$batch_create_custom_vocabulary_item( Foo = 123 ) ## End(Not run)## Not run: svc <- lexmodelsv2() svc$batch_create_custom_vocabulary_item( Foo = 123 ) ## End(Not run)
Amazon Lex provides both build and runtime endpoints. Each endpoint provides a set of operations (API). Your conversational bot uses the runtime API to understand user utterances (user input text or voice). For example, suppose a user says "I want pizza", your bot sends this input to Amazon Lex using the runtime API. Amazon Lex recognizes that the user request is for the OrderPizza intent (one of the intents defined in the bot). Then Amazon Lex engages in user conversation on behalf of the bot to elicit required information (slot values, such as pizza size and crust type), and then performs fulfillment activity (that you configured when you created the bot). You use the build-time API to create and manage your Amazon Lex bot. For a list of build-time operations, see the build-time API, .
lexruntimeservice( config = list(), credentials = list(), endpoint = NULL, region = NULL )lexruntimeservice( config = list(), credentials = list(), endpoint = NULL, region = NULL )
config |
Optional configuration of credentials, endpoint, and/or region.
|
credentials |
Optional credentials shorthand for the config parameter
|
endpoint |
Optional shorthand for complete URL to use for the constructed client. |
region |
Optional shorthand for AWS Region used in instantiating the client. |
A client for the service. You can call the service's operations using
syntax like svc$operation(...), where svc is the name you've assigned
to the client. The available operations are listed in the
Operations section.
svc <- lexruntimeservice(
config = list(
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string",
close_connection = "logical",
timeout = "numeric",
s3_force_path_style = "logical",
sts_regional_endpoint = "string"
),
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string"
)
| delete_session | Removes session information for a specified bot, alias, and user ID |
| get_session | Returns session information for a specified bot, alias, and user ID |
| post_content | Sends user input (text or speech) to Amazon Lex |
| post_text | Sends user input to Amazon Lex |
| put_session | Creates a new session or modifies an existing session with an Amazon Lex bot |
## Not run: svc <- lexruntimeservice() svc$delete_session( Foo = 123 ) ## End(Not run)## Not run: svc <- lexruntimeservice() svc$delete_session( Foo = 123 ) ## End(Not run)
This section contains documentation for the Amazon Lex V2 Runtime V2 API operations.
lexruntimev2( config = list(), credentials = list(), endpoint = NULL, region = NULL )lexruntimev2( config = list(), credentials = list(), endpoint = NULL, region = NULL )
config |
Optional configuration of credentials, endpoint, and/or region.
|
credentials |
Optional credentials shorthand for the config parameter
|
endpoint |
Optional shorthand for complete URL to use for the constructed client. |
region |
Optional shorthand for AWS Region used in instantiating the client. |
A client for the service. You can call the service's operations using
syntax like svc$operation(...), where svc is the name you've assigned
to the client. The available operations are listed in the
Operations section.
svc <- lexruntimev2(
config = list(
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string",
close_connection = "logical",
timeout = "numeric",
s3_force_path_style = "logical",
sts_regional_endpoint = "string"
),
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string"
)
| delete_session | Removes session information for a specified bot, alias, and user ID |
| get_session | Returns session information for a specified bot, alias, and user |
| put_session | Creates a new session or modifies an existing session with an Amazon Lex V2 bot |
| recognize_text | Sends user input to Amazon Lex V2 |
| recognize_utterance | Sends user input to Amazon Lex V2 |
## Not run: svc <- lexruntimev2() svc$delete_session( Foo = 123 ) ## End(Not run)## Not run: svc <- lexruntimev2() svc$delete_session( Foo = 123 ) ## End(Not run)
Amazon Lookout for Equipment is a machine learning service that uses advanced analytics to identify anomalies in machines from sensor data for use in predictive maintenance.
lookoutequipment( config = list(), credentials = list(), endpoint = NULL, region = NULL )lookoutequipment( config = list(), credentials = list(), endpoint = NULL, region = NULL )
config |
Optional configuration of credentials, endpoint, and/or region.
|
credentials |
Optional credentials shorthand for the config parameter
|
endpoint |
Optional shorthand for complete URL to use for the constructed client. |
region |
Optional shorthand for AWS Region used in instantiating the client. |
A client for the service. You can call the service's operations using
syntax like svc$operation(...), where svc is the name you've assigned
to the client. The available operations are listed in the
Operations section.
svc <- lookoutequipment(
config = list(
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string",
close_connection = "logical",
timeout = "numeric",
s3_force_path_style = "logical",
sts_regional_endpoint = "string"
),
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string"
)
| create_dataset | Creates a container for a collection of data being ingested for analysis |
| create_inference_scheduler | Creates a scheduled inference |
| create_label | Creates a label for an event |
| create_label_group | Creates a group of labels |
| create_model | Creates a machine learning model for data inference |
| create_retraining_scheduler | Creates a retraining scheduler on the specified model |
| delete_dataset | Deletes a dataset and associated artifacts |
| delete_inference_scheduler | Deletes an inference scheduler that has been set up |
| delete_label | Deletes a label |
| delete_label_group | Deletes a group of labels |
| delete_model | Deletes a machine learning model currently available for Amazon Lookout for Equipment |
| delete_resource_policy | Deletes the resource policy attached to the resource |
| delete_retraining_scheduler | Deletes a retraining scheduler from a model |
| describe_data_ingestion_job | Provides information on a specific data ingestion job such as creation time, dataset ARN, and status |
| describe_dataset | Provides a JSON description of the data in each time series dataset, including names, column names, and data types |
| describe_inference_scheduler | Specifies information about the inference scheduler being used, including name, model, status, and associated metadata |
| describe_label | Returns the name of the label |
| describe_label_group | Returns information about the label group |
| describe_model | Provides a JSON containing the overall information about a specific machine learning model, including model name and ARN, dataset, training and evaluation information, status, and so on |
| describe_model_version | Retrieves information about a specific machine learning model version |
| describe_resource_policy | Provides the details of a resource policy attached to a resource |
| describe_retraining_scheduler | Provides a description of the retraining scheduler, including information such as the model name and retraining parameters |
| import_dataset | Imports a dataset |
| import_model_version | Imports a model that has been trained successfully |
| list_data_ingestion_jobs | Provides a list of all data ingestion jobs, including dataset name and ARN, S3 location of the input data, status, and so on |
| list_datasets | Lists all datasets currently available in your account, filtering on the dataset name |
| list_inference_events | Lists all inference events that have been found for the specified inference scheduler |
| list_inference_executions | Lists all inference executions that have been performed by the specified inference scheduler |
| list_inference_schedulers | Retrieves a list of all inference schedulers currently available for your account |
| list_label_groups | Returns a list of the label groups |
| list_labels | Provides a list of labels |
| list_models | Generates a list of all models in the account, including model name and ARN, dataset, and status |
| list_model_versions | Generates a list of all model versions for a given model, including the model version, model version ARN, and status |
| list_retraining_schedulers | Lists all retraining schedulers in your account, filtering by model name prefix and status |
| list_sensor_statistics | Lists statistics about the data collected for each of the sensors that have been successfully ingested in the particular dataset |
| list_tags_for_resource | Lists all the tags for a specified resource, including key and value |
| put_resource_policy | Creates a resource control policy for a given resource |
| start_data_ingestion_job | Starts a data ingestion job |
| start_inference_scheduler | Starts an inference scheduler |
| start_retraining_scheduler | Starts a retraining scheduler |
| stop_inference_scheduler | Stops an inference scheduler |
| stop_retraining_scheduler | Stops a retraining scheduler |
| tag_resource | Associates a given tag to a resource in your account |
| untag_resource | Removes a specific tag from a given resource |
| update_active_model_version | Sets the active model version for a given machine learning model |
| update_inference_scheduler | Updates an inference scheduler |
| update_label_group | Updates the label group |
| update_model | Updates a model in the account |
| update_retraining_scheduler | Updates a retraining scheduler |
## Not run: svc <- lookoutequipment() # svc$create_retraining_scheduler( ClientToken = "sample-client-token", LookbackWindow = "P360D", ModelName = "sample-model", PromoteMode = "MANUAL", RetrainingFrequency = "P1M" ) ## End(Not run)## Not run: svc <- lookoutequipment() # svc$create_retraining_scheduler( ClientToken = "sample-client-token", LookbackWindow = "P360D", ModelName = "sample-model", PromoteMode = "MANUAL", RetrainingFrequency = "P1M" ) ## End(Not run)
This is the Amazon Lookout for Metrics API Reference. For an introduction to the service with tutorials for getting started, visit Amazon Lookout for Metrics Developer Guide.
lookoutmetrics( config = list(), credentials = list(), endpoint = NULL, region = NULL )lookoutmetrics( config = list(), credentials = list(), endpoint = NULL, region = NULL )
config |
Optional configuration of credentials, endpoint, and/or region.
|
credentials |
Optional credentials shorthand for the config parameter
|
endpoint |
Optional shorthand for complete URL to use for the constructed client. |
region |
Optional shorthand for AWS Region used in instantiating the client. |
A client for the service. You can call the service's operations using
syntax like svc$operation(...), where svc is the name you've assigned
to the client. The available operations are listed in the
Operations section.
svc <- lookoutmetrics(
config = list(
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string",
close_connection = "logical",
timeout = "numeric",
s3_force_path_style = "logical",
sts_regional_endpoint = "string"
),
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string"
)
| activate_anomaly_detector | Activates an anomaly detector |
| back_test_anomaly_detector | Runs a backtest for anomaly detection for the specified resource |
| create_alert | Creates an alert for an anomaly detector |
| create_anomaly_detector | Creates an anomaly detector |
| create_metric_set | Creates a dataset |
| deactivate_anomaly_detector | Deactivates an anomaly detector |
| delete_alert | Deletes an alert |
| delete_anomaly_detector | Deletes a detector |
| describe_alert | Describes an alert |
| describe_anomaly_detection_executions | Returns information about the status of the specified anomaly detection jobs |
| describe_anomaly_detector | Describes a detector |
| describe_metric_set | Describes a dataset |
| detect_metric_set_config | Detects an Amazon S3 dataset's file format, interval, and offset |
| get_anomaly_group | Returns details about a group of anomalous metrics |
| get_data_quality_metrics | Returns details about the requested data quality metrics |
| get_feedback | Get feedback for an anomaly group |
| get_sample_data | Returns a selection of sample records from an Amazon S3 datasource |
| list_alerts | Lists the alerts attached to a detector |
| list_anomaly_detectors | Lists the detectors in the current AWS Region |
| list_anomaly_group_related_metrics | Returns a list of measures that are potential causes or effects of an anomaly group |
| list_anomaly_group_summaries | Returns a list of anomaly groups |
| list_anomaly_group_time_series | Gets a list of anomalous metrics for a measure in an anomaly group |
| list_metric_sets | Lists the datasets in the current AWS Region |
| list_tags_for_resource | Gets a list of tags for a detector, dataset, or alert |
| put_feedback | Add feedback for an anomalous metric |
| tag_resource | Adds tags to a detector, dataset, or alert |
| untag_resource | Removes tags from a detector, dataset, or alert |
| update_alert | Make changes to an existing alert |
| update_anomaly_detector | Updates a detector |
| update_metric_set | Updates a dataset |
## Not run: svc <- lookoutmetrics() svc$activate_anomaly_detector( Foo = 123 ) ## End(Not run)## Not run: svc <- lookoutmetrics() svc$activate_anomaly_detector( Foo = 123 ) ## End(Not run)
Definition of the public APIs exposed by Amazon Machine Learning
machinelearning( config = list(), credentials = list(), endpoint = NULL, region = NULL )machinelearning( config = list(), credentials = list(), endpoint = NULL, region = NULL )
config |
Optional configuration of credentials, endpoint, and/or region.
|
credentials |
Optional credentials shorthand for the config parameter
|
endpoint |
Optional shorthand for complete URL to use for the constructed client. |
region |
Optional shorthand for AWS Region used in instantiating the client. |
A client for the service. You can call the service's operations using
syntax like svc$operation(...), where svc is the name you've assigned
to the client. The available operations are listed in the
Operations section.
svc <- machinelearning(
config = list(
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string",
close_connection = "logical",
timeout = "numeric",
s3_force_path_style = "logical",
sts_regional_endpoint = "string"
),
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string"
)
| add_tags | Adds one or more tags to an object, up to a limit of 10 |
| create_batch_prediction | Generates predictions for a group of observations |
| create_data_source_from_rds | Creates a DataSource object from an Amazon Relational Database Service (Amazon RDS) |
| create_data_source_from_redshift | Creates a DataSource from a database hosted on an Amazon Redshift cluster |
| create_data_source_from_s3 | Creates a DataSource object |
| create_evaluation | Creates a new Evaluation of an MLModel |
| create_ml_model | Creates a new MLModel using the DataSource and the recipe as information sources |
| create_realtime_endpoint | Creates a real-time endpoint for the MLModel |
| delete_batch_prediction | Assigns the DELETED status to a BatchPrediction, rendering it unusable |
| delete_data_source | Assigns the DELETED status to a DataSource, rendering it unusable |
| delete_evaluation | Assigns the DELETED status to an Evaluation, rendering it unusable |
| delete_ml_model | Assigns the DELETED status to an MLModel, rendering it unusable |
| delete_realtime_endpoint | Deletes a real time endpoint of an MLModel |
| delete_tags | Deletes the specified tags associated with an ML object |
| describe_batch_predictions | Returns a list of BatchPrediction operations that match the search criteria in the request |
| describe_data_sources | Returns a list of DataSource that match the search criteria in the request |
| describe_evaluations | Returns a list of DescribeEvaluations that match the search criteria in the request |
| describe_ml_models | Returns a list of MLModel that match the search criteria in the request |
| describe_tags | Describes one or more of the tags for your Amazon ML object |
| get_batch_prediction | Returns a BatchPrediction that includes detailed metadata, status, and data file information for a Batch Prediction request |
| get_data_source | Returns a DataSource that includes metadata and data file information, as well as the current status of the DataSource |
| get_evaluation | Returns an Evaluation that includes metadata as well as the current status of the Evaluation |
| get_ml_model | Returns an MLModel that includes detailed metadata, data source information, and the current status of the MLModel |
| predict | Generates a prediction for the observation using the specified ML Model |
| update_batch_prediction | Updates the BatchPredictionName of a BatchPrediction |
| update_data_source | Updates the DataSourceName of a DataSource |
| update_evaluation | Updates the EvaluationName of an Evaluation |
| update_ml_model | Updates the MLModelName and the ScoreThreshold of an MLModel |
## Not run: svc <- machinelearning() svc$add_tags( Foo = 123 ) ## End(Not run)## Not run: svc <- machinelearning() svc$add_tags( Foo = 123 ) ## End(Not run)
Overview
This is the AWS Panorama API Reference. For an introduction to the service, see What is AWS Panorama? in the AWS Panorama Developer Guide.
panorama(config = list(), credentials = list(), endpoint = NULL, region = NULL)panorama(config = list(), credentials = list(), endpoint = NULL, region = NULL)
config |
Optional configuration of credentials, endpoint, and/or region.
|
credentials |
Optional credentials shorthand for the config parameter
|
endpoint |
Optional shorthand for complete URL to use for the constructed client. |
region |
Optional shorthand for AWS Region used in instantiating the client. |
A client for the service. You can call the service's operations using
syntax like svc$operation(...), where svc is the name you've assigned
to the client. The available operations are listed in the
Operations section.
svc <- panorama(
config = list(
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string",
close_connection = "logical",
timeout = "numeric",
s3_force_path_style = "logical",
sts_regional_endpoint = "string"
),
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string"
)
| create_application_instance | Creates an application instance and deploys it to a device |
| create_job_for_devices | Creates a job to run on a device |
| create_node_from_template_job | Creates a camera stream node |
| create_package | Creates a package and storage location in an Amazon S3 access point |
| create_package_import_job | Imports a node package |
| delete_device | Deletes a device |
| delete_package | Deletes a package |
| deregister_package_version | Deregisters a package version |
| describe_application_instance | Returns information about an application instance on a device |
| describe_application_instance_details | Returns information about an application instance's configuration manifest |
| describe_device | Returns information about a device |
| describe_device_job | Returns information about a device job |
| describe_node | Returns information about a node |
| describe_node_from_template_job | Returns information about a job to create a camera stream node |
| describe_package | Returns information about a package |
| describe_package_import_job | Returns information about a package import job |
| describe_package_version | Returns information about a package version |
| list_application_instance_dependencies | Returns a list of application instance dependencies |
| list_application_instance_node_instances | Returns a list of application node instances |
| list_application_instances | Returns a list of application instances |
| list_devices | Returns a list of devices |
| list_devices_jobs | Returns a list of jobs |
| list_node_from_template_jobs | Returns a list of camera stream node jobs |
| list_nodes | Returns a list of nodes |
| list_package_import_jobs | Returns a list of package import jobs |
| list_packages | Returns a list of packages |
| list_tags_for_resource | Returns a list of tags for a resource |
| provision_device | Creates a device and returns a configuration archive |
| register_package_version | Registers a package version |
| remove_application_instance | Removes an application instance |
| signal_application_instance_node_instances | Signal camera nodes to stop or resume |
| tag_resource | Tags a resource |
| untag_resource | Removes tags from a resource |
| update_device_metadata | Updates a device's metadata |
## Not run: svc <- panorama() svc$create_application_instance( Foo = 123 ) ## End(Not run)## Not run: svc <- panorama() svc$create_application_instance( Foo = 123 ) ## End(Not run)
Amazon Personalize is a machine learning service that makes it easy to add individualized recommendations to customers.
personalize( config = list(), credentials = list(), endpoint = NULL, region = NULL )personalize( config = list(), credentials = list(), endpoint = NULL, region = NULL )
config |
Optional configuration of credentials, endpoint, and/or region.
|
credentials |
Optional credentials shorthand for the config parameter
|
endpoint |
Optional shorthand for complete URL to use for the constructed client. |
region |
Optional shorthand for AWS Region used in instantiating the client. |
A client for the service. You can call the service's operations using
syntax like svc$operation(...), where svc is the name you've assigned
to the client. The available operations are listed in the
Operations section.
svc <- personalize(
config = list(
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string",
close_connection = "logical",
timeout = "numeric",
s3_force_path_style = "logical",
sts_regional_endpoint = "string"
),
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string"
)
| create_batch_inference_job | Generates batch recommendations based on a list of items or users stored in Amazon S3 and exports the recommendations to an Amazon S3 bucket |
| create_batch_segment_job | Creates a batch segment job |
| create_campaign | You incur campaign costs while it is active |
| create_data_deletion_job | Creates a batch job that deletes all references to specific users from an Amazon Personalize dataset group in batches |
| create_dataset | Creates an empty dataset and adds it to the specified dataset group |
| create_dataset_export_job | Creates a job that exports data from your dataset to an Amazon S3 bucket |
| create_dataset_group | Creates an empty dataset group |
| create_dataset_import_job | Creates a job that imports training data from your data source (an Amazon S3 bucket) to an Amazon Personalize dataset |
| create_event_tracker | Creates an event tracker that you use when adding event data to a specified dataset group using the PutEvents API |
| create_filter | Creates a recommendation filter |
| create_metric_attribution | Creates a metric attribution |
| create_recommender | Creates a recommender with the recipe (a Domain dataset group use case) you specify |
| create_schema | Creates an Amazon Personalize schema from the specified schema string |
| create_solution | By default, all new solutions use automatic training |
| create_solution_version | Trains or retrains an active solution in a Custom dataset group |
| delete_campaign | Removes a campaign by deleting the solution deployment |
| delete_dataset | Deletes a dataset |
| delete_dataset_group | Deletes a dataset group |
| delete_event_tracker | Deletes the event tracker |
| delete_filter | Deletes a filter |
| delete_metric_attribution | Deletes a metric attribution |
| delete_recommender | Deactivates and removes a recommender |
| delete_schema | Deletes a schema |
| delete_solution | Deletes all versions of a solution and the Solution object itself |
| describe_algorithm | Describes the given algorithm |
| describe_batch_inference_job | Gets the properties of a batch inference job including name, Amazon Resource Name (ARN), status, input and output configurations, and the ARN of the solution version used to generate the recommendations |
| describe_batch_segment_job | Gets the properties of a batch segment job including name, Amazon Resource Name (ARN), status, input and output configurations, and the ARN of the solution version used to generate segments |
| describe_campaign | Describes the given campaign, including its status |
| describe_data_deletion_job | Describes the data deletion job created by CreateDataDeletionJob, including the job status |
| describe_dataset | Describes the given dataset |
| describe_dataset_export_job | Describes the dataset export job created by CreateDatasetExportJob, including the export job status |
| describe_dataset_group | Describes the given dataset group |
| describe_dataset_import_job | Describes the dataset import job created by CreateDatasetImportJob, including the import job status |
| describe_event_tracker | Describes an event tracker |
| describe_feature_transformation | Describes the given feature transformation |
| describe_filter | Describes a filter's properties |
| describe_metric_attribution | Describes a metric attribution |
| describe_recipe | Describes a recipe |
| describe_recommender | Describes the given recommender, including its status |
| describe_schema | Describes a schema |
| describe_solution | Describes a solution |
| describe_solution_version | Describes a specific version of a solution |
| get_solution_metrics | Gets the metrics for the specified solution version |
| list_batch_inference_jobs | Gets a list of the batch inference jobs that have been performed off of a solution version |
| list_batch_segment_jobs | Gets a list of the batch segment jobs that have been performed off of a solution version that you specify |
| list_campaigns | Returns a list of campaigns that use the given solution |
| list_data_deletion_jobs | Returns a list of data deletion jobs for a dataset group ordered by creation time, with the most recent first |
| list_dataset_export_jobs | Returns a list of dataset export jobs that use the given dataset |
| list_dataset_groups | Returns a list of dataset groups |
| list_dataset_import_jobs | Returns a list of dataset import jobs that use the given dataset |
| list_datasets | Returns the list of datasets contained in the given dataset group |
| list_event_trackers | Returns the list of event trackers associated with the account |
| list_filters | Lists all filters that belong to a given dataset group |
| list_metric_attribution_metrics | Lists the metrics for the metric attribution |
| list_metric_attributions | Lists metric attributions |
| list_recipes | Returns a list of available recipes |
| list_recommenders | Returns a list of recommenders in a given Domain dataset group |
| list_schemas | Returns the list of schemas associated with the account |
| list_solutions | Returns a list of solutions in a given dataset group |
| list_solution_versions | Returns a list of solution versions for the given solution |
| list_tags_for_resource | Get a list of tags attached to a resource |
| start_recommender | Starts a recommender that is INACTIVE |
| stop_recommender | Stops a recommender that is ACTIVE |
| stop_solution_version_creation | Stops creating a solution version that is in a state of CREATE_PENDING or CREATE IN_PROGRESS |
| tag_resource | Add a list of tags to a resource |
| untag_resource | Removes the specified tags that are attached to a resource |
| update_campaign | Updates a campaign to deploy a retrained solution version with an existing campaign, change your campaign's minProvisionedTPS, or modify your campaign's configuration |
| update_dataset | Update a dataset to replace its schema with a new or existing one |
| update_metric_attribution | Updates a metric attribution |
| update_recommender | Updates the recommender to modify the recommender configuration |
| update_solution | Updates an Amazon Personalize solution to use a different automatic training configuration |
## Not run: svc <- personalize() svc$create_batch_inference_job( Foo = 123 ) ## End(Not run)## Not run: svc <- personalize() svc$create_batch_inference_job( Foo = 123 ) ## End(Not run)
Amazon Personalize can consume real-time user event data, such as stream or click data, and use it for model training either alone or combined with historical data. For more information see Recording item interaction events.
personalizeevents( config = list(), credentials = list(), endpoint = NULL, region = NULL )personalizeevents( config = list(), credentials = list(), endpoint = NULL, region = NULL )
config |
Optional configuration of credentials, endpoint, and/or region.
|
credentials |
Optional credentials shorthand for the config parameter
|
endpoint |
Optional shorthand for complete URL to use for the constructed client. |
region |
Optional shorthand for AWS Region used in instantiating the client. |
A client for the service. You can call the service's operations using
syntax like svc$operation(...), where svc is the name you've assigned
to the client. The available operations are listed in the
Operations section.
svc <- personalizeevents(
config = list(
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string",
close_connection = "logical",
timeout = "numeric",
s3_force_path_style = "logical",
sts_regional_endpoint = "string"
),
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string"
)
| put_action_interactions | Records action interaction event data |
| put_actions | Adds one or more actions to an Actions dataset |
| put_events | Records item interaction event data |
| put_items | Adds one or more items to an Items dataset |
| put_users | Adds one or more users to a Users dataset |
## Not run: svc <- personalizeevents() svc$put_action_interactions( Foo = 123 ) ## End(Not run)## Not run: svc <- personalizeevents() svc$put_action_interactions( Foo = 123 ) ## End(Not run)
Amazon Personalize Runtime
personalizeruntime( config = list(), credentials = list(), endpoint = NULL, region = NULL )personalizeruntime( config = list(), credentials = list(), endpoint = NULL, region = NULL )
config |
Optional configuration of credentials, endpoint, and/or region.
|
credentials |
Optional credentials shorthand for the config parameter
|
endpoint |
Optional shorthand for complete URL to use for the constructed client. |
region |
Optional shorthand for AWS Region used in instantiating the client. |
A client for the service. You can call the service's operations using
syntax like svc$operation(...), where svc is the name you've assigned
to the client. The available operations are listed in the
Operations section.
svc <- personalizeruntime(
config = list(
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string",
close_connection = "logical",
timeout = "numeric",
s3_force_path_style = "logical",
sts_regional_endpoint = "string"
),
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string"
)
| get_action_recommendations | Returns a list of recommended actions in sorted in descending order by prediction score |
| get_personalized_ranking | Re-ranks a list of recommended items for the given user |
| get_recommendations | Returns a list of recommended items |
## Not run: svc <- personalizeruntime() svc$get_action_recommendations( Foo = 123 ) ## End(Not run)## Not run: svc <- personalizeruntime() svc$get_action_recommendations( Foo = 123 ) ## End(Not run)
Amazon Polly is a web service that makes it easy to synthesize speech from text.
The Amazon Polly service provides API operations for synthesizing high-quality speech from plain text and Speech Synthesis Markup Language (SSML), along with managing pronunciations lexicons that enable you to get the best results for your application domain.
polly(config = list(), credentials = list(), endpoint = NULL, region = NULL)polly(config = list(), credentials = list(), endpoint = NULL, region = NULL)
config |
Optional configuration of credentials, endpoint, and/or region.
|
credentials |
Optional credentials shorthand for the config parameter
|
endpoint |
Optional shorthand for complete URL to use for the constructed client. |
region |
Optional shorthand for AWS Region used in instantiating the client. |
A client for the service. You can call the service's operations using
syntax like svc$operation(...), where svc is the name you've assigned
to the client. The available operations are listed in the
Operations section.
svc <- polly(
config = list(
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string",
close_connection = "logical",
timeout = "numeric",
s3_force_path_style = "logical",
sts_regional_endpoint = "string"
),
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string"
)
| delete_lexicon | Deletes the specified pronunciation lexicon stored in an Amazon Web Services Region |
| describe_voices | Returns the list of voices that are available for use when requesting speech synthesis |
| get_lexicon | Returns the content of the specified pronunciation lexicon stored in an Amazon Web Services Region |
| get_speech_synthesis_task | Retrieves a specific SpeechSynthesisTask object based on its TaskID |
| list_lexicons | Returns a list of pronunciation lexicons stored in an Amazon Web Services Region |
| list_speech_synthesis_tasks | Returns a list of SpeechSynthesisTask objects ordered by their creation date |
| put_lexicon | Stores a pronunciation lexicon in an Amazon Web Services Region |
| start_speech_synthesis_task | Allows the creation of an asynchronous synthesis task, by starting a new SpeechSynthesisTask |
| synthesize_speech | Synthesizes UTF-8 input, plain text or SSML, to a stream of bytes |
## Not run: svc <- polly() # Deletes a specified pronunciation lexicon stored in an AWS Region. svc$delete_lexicon( Name = "example" ) ## End(Not run)## Not run: svc <- polly() # Deletes a specified pronunciation lexicon stored in an AWS Region. svc$delete_lexicon( Name = "example" ) ## End(Not run)
This is the API Reference for Amazon Rekognition Image, Amazon Rekognition Custom Labels, Amazon Rekognition Stored Video, Amazon Rekognition Streaming Video. It provides descriptions of actions, data types, common parameters, and common errors.
Amazon Rekognition Image
Amazon Rekognition Custom Labels
Amazon Rekognition Video Stored Video
Amazon Rekognition Video Streaming Video
rekognition( config = list(), credentials = list(), endpoint = NULL, region = NULL )rekognition( config = list(), credentials = list(), endpoint = NULL, region = NULL )
config |
Optional configuration of credentials, endpoint, and/or region.
|
credentials |
Optional credentials shorthand for the config parameter
|
endpoint |
Optional shorthand for complete URL to use for the constructed client. |
region |
Optional shorthand for AWS Region used in instantiating the client. |
A client for the service. You can call the service's operations using
syntax like svc$operation(...), where svc is the name you've assigned
to the client. The available operations are listed in the
Operations section.
svc <- rekognition(
config = list(
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string",
close_connection = "logical",
timeout = "numeric",
s3_force_path_style = "logical",
sts_regional_endpoint = "string"
),
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string"
)
| associate_faces | Associates one or more faces with an existing UserID |
| compare_faces | Compares a face in the source input image with each of the 100 largest faces detected in the target input image |
| copy_project_version | This operation applies only to Amazon Rekognition Custom Labels |
| create_collection | Creates a collection in an AWS Region |
| create_dataset | This operation applies only to Amazon Rekognition Custom Labels |
| create_face_liveness_session | This API operation initiates a Face Liveness session |
| create_project | Creates a new Amazon Rekognition project |
| create_project_version | Creates a new version of Amazon Rekognition project (like a Custom Labels model or a custom adapter) and begins training |
| create_stream_processor | Creates an Amazon Rekognition stream processor that you can use to detect and recognize faces or to detect labels in a streaming video |
| create_user | Creates a new User within a collection specified by CollectionId |
| delete_collection | Deletes the specified collection |
| delete_dataset | This operation applies only to Amazon Rekognition Custom Labels |
| delete_faces | Deletes faces from a collection |
| delete_project | Deletes a Amazon Rekognition project |
| delete_project_policy | This operation applies only to Amazon Rekognition Custom Labels |
| delete_project_version | Deletes a Rekognition project model or project version, like a Amazon Rekognition Custom Labels model or a custom adapter |
| delete_stream_processor | Deletes the stream processor identified by Name |
| delete_user | Deletes the specified UserID within the collection |
| describe_collection | Describes the specified collection |
| describe_dataset | This operation applies only to Amazon Rekognition Custom Labels |
| describe_projects | Gets information about your Rekognition projects |
| describe_project_versions | Lists and describes the versions of an Amazon Rekognition project |
| describe_stream_processor | Provides information about a stream processor created by CreateStreamProcessor |
| detect_custom_labels | This operation applies only to Amazon Rekognition Custom Labels |
| detect_faces | Detects faces within an image that is provided as input |
| detect_labels | Detects instances of real-world entities within an image (JPEG or PNG) provided as input |
| detect_moderation_labels | Detects unsafe content in a specified JPEG or PNG format image |
| detect_protective_equipment | Detects Personal Protective Equipment (PPE) worn by people detected in an image |
| detect_text | Detects text in the input image and converts it into machine-readable text |
| disassociate_faces | Removes the association between a Face supplied in an array of FaceIds and the User |
| distribute_dataset_entries | This operation applies only to Amazon Rekognition Custom Labels |
| get_celebrity_info | Gets the name and additional information about a celebrity based on their Amazon Rekognition ID |
| get_celebrity_recognition | Gets the celebrity recognition results for a Amazon Rekognition Video analysis started by StartCelebrityRecognition |
| get_content_moderation | Gets the inappropriate, unwanted, or offensive content analysis results for a Amazon Rekognition Video analysis started by StartContentModeration |
| get_face_detection | Gets face detection results for a Amazon Rekognition Video analysis started by StartFaceDetection |
| get_face_liveness_session_results | Retrieves the results of a specific Face Liveness session |
| get_face_search | Gets the face search results for Amazon Rekognition Video face search started by StartFaceSearch |
| get_label_detection | Gets the label detection results of a Amazon Rekognition Video analysis started by StartLabelDetection |
| get_media_analysis_job | Retrieves the results for a given media analysis job |
| get_person_tracking | Gets the path tracking results of a Amazon Rekognition Video analysis started by StartPersonTracking |
| get_segment_detection | Gets the segment detection results of a Amazon Rekognition Video analysis started by StartSegmentDetection |
| get_text_detection | Gets the text detection results of a Amazon Rekognition Video analysis started by StartTextDetection |
| index_faces | Detects faces in the input image and adds them to the specified collection |
| list_collections | Returns list of collection IDs in your account |
| list_dataset_entries | This operation applies only to Amazon Rekognition Custom Labels |
| list_dataset_labels | This operation applies only to Amazon Rekognition Custom Labels |
| list_faces | Returns metadata for faces in the specified collection |
| list_media_analysis_jobs | Returns a list of media analysis jobs |
| list_project_policies | This operation applies only to Amazon Rekognition Custom Labels |
| list_stream_processors | Gets a list of stream processors that you have created with CreateStreamProcessor |
| list_tags_for_resource | Returns a list of tags in an Amazon Rekognition collection, stream processor, or Custom Labels model |
| list_users | Returns metadata of the User such as UserID in the specified collection |
| put_project_policy | This operation applies only to Amazon Rekognition Custom Labels |
| recognize_celebrities | Returns an array of celebrities recognized in the input image |
| search_faces | For a given input face ID, searches for matching faces in the collection the face belongs to |
| search_faces_by_image | For a given input image, first detects the largest face in the image, and then searches the specified collection for matching faces |
| search_users | Searches for UserIDs within a collection based on a FaceId or UserId |
| search_users_by_image | Searches for UserIDs using a supplied image |
| start_celebrity_recognition | Starts asynchronous recognition of celebrities in a stored video |
| start_content_moderation | Starts asynchronous detection of inappropriate, unwanted, or offensive content in a stored video |
| start_face_detection | Starts asynchronous detection of faces in a stored video |
| start_face_search | Starts the asynchronous search for faces in a collection that match the faces of persons detected in a stored video |
| start_label_detection | Starts asynchronous detection of labels in a stored video |
| start_media_analysis_job | Initiates a new media analysis job |
| start_person_tracking | Starts the asynchronous tracking of a person's path in a stored video |
| start_project_version | This operation applies only to Amazon Rekognition Custom Labels |
| start_segment_detection | Starts asynchronous detection of segment detection in a stored video |
| start_stream_processor | Starts processing a stream processor |
| start_text_detection | Starts asynchronous detection of text in a stored video |
| stop_project_version | This operation applies only to Amazon Rekognition Custom Labels |
| stop_stream_processor | Stops a running stream processor that was created by CreateStreamProcessor |
| tag_resource | Adds one or more key-value tags to an Amazon Rekognition collection, stream processor, or Custom Labels model |
| untag_resource | Removes one or more tags from an Amazon Rekognition collection, stream processor, or Custom Labels model |
| update_dataset_entries | This operation applies only to Amazon Rekognition Custom Labels |
| update_stream_processor | Allows you to update a stream processor |
## Not run: svc <- rekognition() # This operation associates one or more faces with an existing UserID. svc$associate_faces( ClientRequestToken = "550e8400-e29b-41d4-a716-446655440002", CollectionId = "MyCollection", FaceIds = list( "f5817d37-94f6-4335-bfee-6cf79a3d806e", "851cb847-dccc-4fea-9309-9f4805967855", "35ebbb41-7f67-4263-908d-dd0ecba05ab9" ), UserId = "DemoUser", UserMatchThreshold = 70L ) ## End(Not run)## Not run: svc <- rekognition() # This operation associates one or more faces with an existing UserID. svc$associate_faces( ClientRequestToken = "550e8400-e29b-41d4-a716-446655440002", CollectionId = "MyCollection", FaceIds = list( "f5817d37-94f6-4335-bfee-6cf79a3d806e", "851cb847-dccc-4fea-9309-9f4805967855", "35ebbb41-7f67-4263-908d-dd0ecba05ab9" ), UserId = "DemoUser", UserMatchThreshold = 70L ) ## End(Not run)
Provides APIs for creating and managing SageMaker resources.
Other Resources:
sagemaker( config = list(), credentials = list(), endpoint = NULL, region = NULL )sagemaker( config = list(), credentials = list(), endpoint = NULL, region = NULL )
config |
Optional configuration of credentials, endpoint, and/or region.
|
credentials |
Optional credentials shorthand for the config parameter
|
endpoint |
Optional shorthand for complete URL to use for the constructed client. |
region |
Optional shorthand for AWS Region used in instantiating the client. |
A client for the service. You can call the service's operations using
syntax like svc$operation(...), where svc is the name you've assigned
to the client. The available operations are listed in the
Operations section.
svc <- sagemaker(
config = list(
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string",
close_connection = "logical",
timeout = "numeric",
s3_force_path_style = "logical",
sts_regional_endpoint = "string"
),
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string"
)
| add_association | Creates an association between the source and the destination |
| add_tags | Adds or overwrites one or more tags for the specified SageMaker resource |
| associate_trial_component | Associates a trial component with a trial |
| batch_describe_model_package | This action batch describes a list of versioned model packages |
| create_action | Creates an action |
| create_algorithm | Create a machine learning algorithm that you can use in SageMaker and list in the Amazon Web Services Marketplace |
| create_app | Creates a running app for the specified UserProfile |
| create_app_image_config | Creates a configuration for running a SageMaker image as a KernelGateway app |
| create_artifact | Creates an artifact |
| create_auto_ml_job | Creates an Autopilot job also referred to as Autopilot experiment or AutoML job |
| create_auto_ml_job_v2 | Creates an Autopilot job also referred to as Autopilot experiment or AutoML job V2 |
| create_cluster | Creates a SageMaker HyperPod cluster |
| create_code_repository | Creates a Git repository as a resource in your SageMaker account |
| create_compilation_job | Starts a model compilation job |
| create_context | Creates a context |
| create_data_quality_job_definition | Creates a definition for a job that monitors data quality and drift |
| create_device_fleet | Creates a device fleet |
| create_domain | Creates a Domain |
| create_edge_deployment_plan | Creates an edge deployment plan, consisting of multiple stages |
| create_edge_deployment_stage | Creates a new stage in an existing edge deployment plan |
| create_edge_packaging_job | Starts a SageMaker Edge Manager model packaging job |
| create_endpoint | Creates an endpoint using the endpoint configuration specified in the request |
| create_endpoint_config | Creates an endpoint configuration that SageMaker hosting services uses to deploy models |
| create_experiment | Creates a SageMaker experiment |
| create_feature_group | Create a new FeatureGroup |
| create_flow_definition | Creates a flow definition |
| create_hub | Create a hub |
| create_hub_content_reference | Create a hub content reference in order to add a model in the JumpStart public hub to a private hub |
| create_human_task_ui | Defines the settings you will use for the human review workflow user interface |
| create_hyper_parameter_tuning_job | Starts a hyperparameter tuning job |
| create_image | Creates a custom SageMaker image |
| create_image_version | Creates a version of the SageMaker image specified by ImageName |
| create_inference_component | Creates an inference component, which is a SageMaker hosting object that you can use to deploy a model to an endpoint |
| create_inference_experiment | Creates an inference experiment using the configurations specified in the request |
| create_inference_recommendations_job | Starts a recommendation job |
| create_labeling_job | Creates a job that uses workers to label the data objects in your input dataset |
| create_mlflow_tracking_server | Creates an MLflow Tracking Server using a general purpose Amazon S3 bucket as the artifact store |
| create_model | Creates a model in SageMaker |
| create_model_bias_job_definition | Creates the definition for a model bias job |
| create_model_card | Creates an Amazon SageMaker Model Card |
| create_model_card_export_job | Creates an Amazon SageMaker Model Card export job |
| create_model_explainability_job_definition | Creates the definition for a model explainability job |
| create_model_package | Creates a model package that you can use to create SageMaker models or list on Amazon Web Services Marketplace, or a versioned model that is part of a model group |
| create_model_package_group | Creates a model group |
| create_model_quality_job_definition | Creates a definition for a job that monitors model quality and drift |
| create_monitoring_schedule | Creates a schedule that regularly starts Amazon SageMaker Processing Jobs to monitor the data captured for an Amazon SageMaker Endpoint |
| create_notebook_instance | Creates an SageMaker notebook instance |
| create_notebook_instance_lifecycle_config | Creates a lifecycle configuration that you can associate with a notebook instance |
| create_optimization_job | Creates a job that optimizes a model for inference performance |
| create_pipeline | Creates a pipeline using a JSON pipeline definition |
| create_presigned_domain_url | Creates a URL for a specified UserProfile in a Domain |
| create_presigned_mlflow_tracking_server_url | Returns a presigned URL that you can use to connect to the MLflow UI attached to your tracking server |
| create_presigned_notebook_instance_url | Returns a URL that you can use to connect to the Jupyter server from a notebook instance |
| create_processing_job | Creates a processing job |
| create_project | Creates a machine learning (ML) project that can contain one or more templates that set up an ML pipeline from training to deploying an approved model |
| create_space | Creates a private space or a space used for real time collaboration in a domain |
| create_studio_lifecycle_config | Creates a new Amazon SageMaker Studio Lifecycle Configuration |
| create_training_job | Starts a model training job |
| create_transform_job | Starts a transform job |
| create_trial | Creates an SageMaker trial |
| create_trial_component | Creates a trial component, which is a stage of a machine learning trial |
| create_user_profile | Creates a user profile |
| create_workforce | Use this operation to create a workforce |
| create_workteam | Creates a new work team for labeling your data |
| delete_action | Deletes an action |
| delete_algorithm | Removes the specified algorithm from your account |
| delete_app | Used to stop and delete an app |
| delete_app_image_config | Deletes an AppImageConfig |
| delete_artifact | Deletes an artifact |
| delete_association | Deletes an association |
| delete_cluster | Delete a SageMaker HyperPod cluster |
| delete_code_repository | Deletes the specified Git repository from your account |
| delete_compilation_job | Deletes the specified compilation job |
| delete_context | Deletes an context |
| delete_data_quality_job_definition | Deletes a data quality monitoring job definition |
| delete_device_fleet | Deletes a fleet |
| delete_domain | Used to delete a domain |
| delete_edge_deployment_plan | Deletes an edge deployment plan if (and only if) all the stages in the plan are inactive or there are no stages in the plan |
| delete_edge_deployment_stage | Delete a stage in an edge deployment plan if (and only if) the stage is inactive |
| delete_endpoint | Deletes an endpoint |
| delete_endpoint_config | Deletes an endpoint configuration |
| delete_experiment | Deletes an SageMaker experiment |
| delete_feature_group | Delete the FeatureGroup and any data that was written to the OnlineStore of the FeatureGroup |
| delete_flow_definition | Deletes the specified flow definition |
| delete_hub | Delete a hub |
| delete_hub_content | Delete the contents of a hub |
| delete_hub_content_reference | Delete a hub content reference in order to remove a model from a private hub |
| delete_human_task_ui | Use this operation to delete a human task user interface (worker task template) |
| delete_hyper_parameter_tuning_job | Deletes a hyperparameter tuning job |
| delete_image | Deletes a SageMaker image and all versions of the image |
| delete_image_version | Deletes a version of a SageMaker image |
| delete_inference_component | Deletes an inference component |
| delete_inference_experiment | Deletes an inference experiment |
| delete_mlflow_tracking_server | Deletes an MLflow Tracking Server |
| delete_model | Deletes a model |
| delete_model_bias_job_definition | Deletes an Amazon SageMaker model bias job definition |
| delete_model_card | Deletes an Amazon SageMaker Model Card |
| delete_model_explainability_job_definition | Deletes an Amazon SageMaker model explainability job definition |
| delete_model_package | Deletes a model package |
| delete_model_package_group | Deletes the specified model group |
| delete_model_package_group_policy | Deletes a model group resource policy |
| delete_model_quality_job_definition | Deletes the secified model quality monitoring job definition |
| delete_monitoring_schedule | Deletes a monitoring schedule |
| delete_notebook_instance | Deletes an SageMaker notebook instance |
| delete_notebook_instance_lifecycle_config | Deletes a notebook instance lifecycle configuration |
| delete_optimization_job | Deletes an optimization job |
| delete_pipeline | Deletes a pipeline if there are no running instances of the pipeline |
| delete_project | Delete the specified project |
| delete_space | Used to delete a space |
| delete_studio_lifecycle_config | Deletes the Amazon SageMaker Studio Lifecycle Configuration |
| delete_tags | Deletes the specified tags from an SageMaker resource |
| delete_trial | Deletes the specified trial |
| delete_trial_component | Deletes the specified trial component |
| delete_user_profile | Deletes a user profile |
| delete_workforce | Use this operation to delete a workforce |
| delete_workteam | Deletes an existing work team |
| deregister_devices | Deregisters the specified devices |
| describe_action | Describes an action |
| describe_algorithm | Returns a description of the specified algorithm that is in your account |
| describe_app | Describes the app |
| describe_app_image_config | Describes an AppImageConfig |
| describe_artifact | Describes an artifact |
| describe_auto_ml_job | Returns information about an AutoML job created by calling CreateAutoMLJob |
| describe_auto_ml_job_v2 | Returns information about an AutoML job created by calling CreateAutoMLJobV2 or CreateAutoMLJob |
| describe_cluster | Retrieves information of a SageMaker HyperPod cluster |
| describe_cluster_node | Retrieves information of a node (also called a instance interchangeably) of a SageMaker HyperPod cluster |
| describe_code_repository | Gets details about the specified Git repository |
| describe_compilation_job | Returns information about a model compilation job |
| describe_context | Describes a context |
| describe_data_quality_job_definition | Gets the details of a data quality monitoring job definition |
| describe_device | Describes the device |
| describe_device_fleet | A description of the fleet the device belongs to |
| describe_domain | The description of the domain |
| describe_edge_deployment_plan | Describes an edge deployment plan with deployment status per stage |
| describe_edge_packaging_job | A description of edge packaging jobs |
| describe_endpoint | Returns the description of an endpoint |
| describe_endpoint_config | Returns the description of an endpoint configuration created using the CreateEndpointConfig API |
| describe_experiment | Provides a list of an experiment's properties |
| describe_feature_group | Use this operation to describe a FeatureGroup |
| describe_feature_metadata | Shows the metadata for a feature within a feature group |
| describe_flow_definition | Returns information about the specified flow definition |
| describe_hub | Describes a hub |
| describe_hub_content | Describe the content of a hub |
| describe_human_task_ui | Returns information about the requested human task user interface (worker task template) |
| describe_hyper_parameter_tuning_job | Returns a description of a hyperparameter tuning job, depending on the fields selected |
| describe_image | Describes a SageMaker image |
| describe_image_version | Describes a version of a SageMaker image |
| describe_inference_component | Returns information about an inference component |
| describe_inference_experiment | Returns details about an inference experiment |
| describe_inference_recommendations_job | Provides the results of the Inference Recommender job |
| describe_labeling_job | Gets information about a labeling job |
| describe_lineage_group | Provides a list of properties for the requested lineage group |
| describe_mlflow_tracking_server | Returns information about an MLflow Tracking Server |
| describe_model | Describes a model that you created using the CreateModel API |
| describe_model_bias_job_definition | Returns a description of a model bias job definition |
| describe_model_card | Describes the content, creation time, and security configuration of an Amazon SageMaker Model Card |
| describe_model_card_export_job | Describes an Amazon SageMaker Model Card export job |
| describe_model_explainability_job_definition | Returns a description of a model explainability job definition |
| describe_model_package | Returns a description of the specified model package, which is used to create SageMaker models or list them on Amazon Web Services Marketplace |
| describe_model_package_group | Gets a description for the specified model group |
| describe_model_quality_job_definition | Returns a description of a model quality job definition |
| describe_monitoring_schedule | Describes the schedule for a monitoring job |
| describe_notebook_instance | Returns information about a notebook instance |
| describe_notebook_instance_lifecycle_config | Returns a description of a notebook instance lifecycle configuration |
| describe_optimization_job | Provides the properties of the specified optimization job |
| describe_pipeline | Describes the details of a pipeline |
| describe_pipeline_definition_for_execution | Describes the details of an execution's pipeline definition |
| describe_pipeline_execution | Describes the details of a pipeline execution |
| describe_processing_job | Returns a description of a processing job |
| describe_project | Describes the details of a project |
| describe_space | Describes the space |
| describe_studio_lifecycle_config | Describes the Amazon SageMaker Studio Lifecycle Configuration |
| describe_subscribed_workteam | Gets information about a work team provided by a vendor |
| describe_training_job | Returns information about a training job |
| describe_transform_job | Returns information about a transform job |
| describe_trial | Provides a list of a trial's properties |
| describe_trial_component | Provides a list of a trials component's properties |
| describe_user_profile | Describes a user profile |
| describe_workforce | Lists private workforce information, including workforce name, Amazon Resource Name (ARN), and, if applicable, allowed IP address ranges (CIDRs) |
| describe_workteam | Gets information about a specific work team |
| disable_sagemaker_servicecatalog_portfolio | Disables using Service Catalog in SageMaker |
| disassociate_trial_component | Disassociates a trial component from a trial |
| enable_sagemaker_servicecatalog_portfolio | Enables using Service Catalog in SageMaker |
| get_device_fleet_report | Describes a fleet |
| get_lineage_group_policy | The resource policy for the lineage group |
| get_model_package_group_policy | Gets a resource policy that manages access for a model group |
| get_sagemaker_servicecatalog_portfolio_status | Gets the status of Service Catalog in SageMaker |
| get_scaling_configuration_recommendation | Starts an Amazon SageMaker Inference Recommender autoscaling recommendation job |
| get_search_suggestions | An auto-complete API for the search functionality in the SageMaker console |
| import_hub_content | Import hub content |
| list_actions | Lists the actions in your account and their properties |
| list_algorithms | Lists the machine learning algorithms that have been created |
| list_aliases | Lists the aliases of a specified image or image version |
| list_app_image_configs | Lists the AppImageConfigs in your account and their properties |
| list_apps | Lists apps |
| list_artifacts | Lists the artifacts in your account and their properties |
| list_associations | Lists the associations in your account and their properties |
| list_auto_ml_jobs | Request a list of jobs |
| list_candidates_for_auto_ml_job | List the candidates created for the job |
| list_cluster_nodes | Retrieves the list of instances (also called nodes interchangeably) in a SageMaker HyperPod cluster |
| list_clusters | Retrieves the list of SageMaker HyperPod clusters |
| list_code_repositories | Gets a list of the Git repositories in your account |
| list_compilation_jobs | Lists model compilation jobs that satisfy various filters |
| list_contexts | Lists the contexts in your account and their properties |
| list_data_quality_job_definitions | Lists the data quality job definitions in your account |
| list_device_fleets | Returns a list of devices in the fleet |
| list_devices | A list of devices |
| list_domains | Lists the domains |
| list_edge_deployment_plans | Lists all edge deployment plans |
| list_edge_packaging_jobs | Returns a list of edge packaging jobs |
| list_endpoint_configs | Lists endpoint configurations |
| list_endpoints | Lists endpoints |
| list_experiments | Lists all the experiments in your account |
| list_feature_groups | List FeatureGroups based on given filter and order |
| list_flow_definitions | Returns information about the flow definitions in your account |
| list_hub_contents | List the contents of a hub |
| list_hub_content_versions | List hub content versions |
| list_hubs | List all existing hubs |
| list_human_task_uis | Returns information about the human task user interfaces in your account |
| list_hyper_parameter_tuning_jobs | Gets a list of HyperParameterTuningJobSummary objects that describe the hyperparameter tuning jobs launched in your account |
| list_images | Lists the images in your account and their properties |
| list_image_versions | Lists the versions of a specified image and their properties |
| list_inference_components | Lists the inference components in your account and their properties |
| list_inference_experiments | Returns the list of all inference experiments |
| list_inference_recommendations_jobs | Lists recommendation jobs that satisfy various filters |
| list_inference_recommendations_job_steps | Returns a list of the subtasks for an Inference Recommender job |
| list_labeling_jobs | Gets a list of labeling jobs |
| list_labeling_jobs_for_workteam | Gets a list of labeling jobs assigned to a specified work team |
| list_lineage_groups | A list of lineage groups shared with your Amazon Web Services account |
| list_mlflow_tracking_servers | Lists all MLflow Tracking Servers |
| list_model_bias_job_definitions | Lists model bias jobs definitions that satisfy various filters |
| list_model_card_export_jobs | List the export jobs for the Amazon SageMaker Model Card |
| list_model_cards | List existing model cards |
| list_model_card_versions | List existing versions of an Amazon SageMaker Model Card |
| list_model_explainability_job_definitions | Lists model explainability job definitions that satisfy various filters |
| list_model_metadata | Lists the domain, framework, task, and model name of standard machine learning models found in common model zoos |
| list_model_package_groups | Gets a list of the model groups in your Amazon Web Services account |
| list_model_packages | Lists the model packages that have been created |
| list_model_quality_job_definitions | Gets a list of model quality monitoring job definitions in your account |
| list_models | Lists models created with the CreateModel API |
| list_monitoring_alert_history | Gets a list of past alerts in a model monitoring schedule |
| list_monitoring_alerts | Gets the alerts for a single monitoring schedule |
| list_monitoring_executions | Returns list of all monitoring job executions |
| list_monitoring_schedules | Returns list of all monitoring schedules |
| list_notebook_instance_lifecycle_configs | Lists notebook instance lifestyle configurations created with the CreateNotebookInstanceLifecycleConfig API |
| list_notebook_instances | Returns a list of the SageMaker notebook instances in the requester's account in an Amazon Web Services Region |
| list_optimization_jobs | Lists the optimization jobs in your account and their properties |
| list_pipeline_executions | Gets a list of the pipeline executions |
| list_pipeline_execution_steps | Gets a list of PipeLineExecutionStep objects |
| list_pipeline_parameters_for_execution | Gets a list of parameters for a pipeline execution |
| list_pipelines | Gets a list of pipelines |
| list_processing_jobs | Lists processing jobs that satisfy various filters |
| list_projects | Gets a list of the projects in an Amazon Web Services account |
| list_resource_catalogs | Lists Amazon SageMaker Catalogs based on given filters and orders |
| list_spaces | Lists spaces |
| list_stage_devices | Lists devices allocated to the stage, containing detailed device information and deployment status |
| list_studio_lifecycle_configs | Lists the Amazon SageMaker Studio Lifecycle Configurations in your Amazon Web Services Account |
| list_subscribed_workteams | Gets a list of the work teams that you are subscribed to in the Amazon Web Services Marketplace |
| list_tags | Returns the tags for the specified SageMaker resource |
| list_training_jobs | Lists training jobs |
| list_training_jobs_for_hyper_parameter_tuning_job | Gets a list of TrainingJobSummary objects that describe the training jobs that a hyperparameter tuning job launched |
| list_transform_jobs | Lists transform jobs |
| list_trial_components | Lists the trial components in your account |
| list_trials | Lists the trials in your account |
| list_user_profiles | Lists user profiles |
| list_workforces | Use this operation to list all private and vendor workforces in an Amazon Web Services Region |
| list_workteams | Gets a list of private work teams that you have defined in a region |
| put_model_package_group_policy | Adds a resouce policy to control access to a model group |
| query_lineage | Use this action to inspect your lineage and discover relationships between entities |
| register_devices | Register devices |
| render_ui_template | Renders the UI template so that you can preview the worker's experience |
| retry_pipeline_execution | Retry the execution of the pipeline |
| search | Finds SageMaker resources that match a search query |
| send_pipeline_execution_step_failure | Notifies the pipeline that the execution of a callback step failed, along with a message describing why |
| send_pipeline_execution_step_success | Notifies the pipeline that the execution of a callback step succeeded and provides a list of the step's output parameters |
| start_edge_deployment_stage | Starts a stage in an edge deployment plan |
| start_inference_experiment | Starts an inference experiment |
| start_mlflow_tracking_server | Programmatically start an MLflow Tracking Server |
| start_monitoring_schedule | Starts a previously stopped monitoring schedule |
| start_notebook_instance | Launches an ML compute instance with the latest version of the libraries and attaches your ML storage volume |
| start_pipeline_execution | Starts a pipeline execution |
| stop_auto_ml_job | A method for forcing a running job to shut down |
| stop_compilation_job | Stops a model compilation job |
| stop_edge_deployment_stage | Stops a stage in an edge deployment plan |
| stop_edge_packaging_job | Request to stop an edge packaging job |
| stop_hyper_parameter_tuning_job | Stops a running hyperparameter tuning job and all running training jobs that the tuning job launched |
| stop_inference_experiment | Stops an inference experiment |
| stop_inference_recommendations_job | Stops an Inference Recommender job |
| stop_labeling_job | Stops a running labeling job |
| stop_mlflow_tracking_server | Programmatically stop an MLflow Tracking Server |
| stop_monitoring_schedule | Stops a previously started monitoring schedule |
| stop_notebook_instance | Terminates the ML compute instance |
| stop_optimization_job | Ends a running inference optimization job |
| stop_pipeline_execution | Stops a pipeline execution |
| stop_processing_job | Stops a processing job |
| stop_training_job | Stops a training job |
| stop_transform_job | Stops a batch transform job |
| update_action | Updates an action |
| update_app_image_config | Updates the properties of an AppImageConfig |
| update_artifact | Updates an artifact |
| update_cluster | Updates a SageMaker HyperPod cluster |
| update_cluster_software | Updates the platform software of a SageMaker HyperPod cluster for security patching |
| update_code_repository | Updates the specified Git repository with the specified values |
| update_context | Updates a context |
| update_device_fleet | Updates a fleet of devices |
| update_devices | Updates one or more devices in a fleet |
| update_domain | Updates the default settings for new user profiles in the domain |
| update_endpoint | Deploys the EndpointConfig specified in the request to a new fleet of instances |
| update_endpoint_weights_and_capacities | Updates variant weight of one or more variants associated with an existing endpoint, or capacity of one variant associated with an existing endpoint |
| update_experiment | Adds, updates, or removes the description of an experiment |
| update_feature_group | Updates the feature group by either adding features or updating the online store configuration |
| update_feature_metadata | Updates the description and parameters of the feature group |
| update_hub | Update a hub |
| update_image | Updates the properties of a SageMaker image |
| update_image_version | Updates the properties of a SageMaker image version |
| update_inference_component | Updates an inference component |
| update_inference_component_runtime_config | Runtime settings for a model that is deployed with an inference component |
| update_inference_experiment | Updates an inference experiment that you created |
| update_mlflow_tracking_server | Updates properties of an existing MLflow Tracking Server |
| update_model_card | Update an Amazon SageMaker Model Card |
| update_model_package | Updates a versioned model |
| update_monitoring_alert | Update the parameters of a model monitor alert |
| update_monitoring_schedule | Updates a previously created schedule |
| update_notebook_instance | Updates a notebook instance |
| update_notebook_instance_lifecycle_config | Updates a notebook instance lifecycle configuration created with the CreateNotebookInstanceLifecycleConfig API |
| update_pipeline | Updates a pipeline |
| update_pipeline_execution | Updates a pipeline execution |
| update_project | Updates a machine learning (ML) project that is created from a template that sets up an ML pipeline from training to deploying an approved model |
| update_space | Updates the settings of a space |
| update_training_job | Update a model training job to request a new Debugger profiling configuration or to change warm pool retention length |
| update_trial | Updates the display name of a trial |
| update_trial_component | Updates one or more properties of a trial component |
| update_user_profile | Updates a user profile |
| update_workforce | Use this operation to update your workforce |
| update_workteam | Updates an existing work team with new member definitions or description |
## Not run: svc <- sagemaker() svc$add_association( Foo = 123 ) ## End(Not run)## Not run: svc <- sagemaker() svc$add_association( Foo = 123 ) ## End(Not run)
SageMaker Edge Manager dataplane service for communicating with active agents.
sagemakeredgemanager( config = list(), credentials = list(), endpoint = NULL, region = NULL )sagemakeredgemanager( config = list(), credentials = list(), endpoint = NULL, region = NULL )
config |
Optional configuration of credentials, endpoint, and/or region.
|
credentials |
Optional credentials shorthand for the config parameter
|
endpoint |
Optional shorthand for complete URL to use for the constructed client. |
region |
Optional shorthand for AWS Region used in instantiating the client. |
A client for the service. You can call the service's operations using
syntax like svc$operation(...), where svc is the name you've assigned
to the client. The available operations are listed in the
Operations section.
svc <- sagemakeredgemanager(
config = list(
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string",
close_connection = "logical",
timeout = "numeric",
s3_force_path_style = "logical",
sts_regional_endpoint = "string"
),
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string"
)
| get_deployments | Use to get the active deployments from a device |
| get_device_registration | Use to check if a device is registered with SageMaker Edge Manager |
| send_heartbeat | Use to get the current status of devices registered on SageMaker Edge Manager |
## Not run: svc <- sagemakeredgemanager() svc$get_deployments( Foo = 123 ) ## End(Not run)## Not run: svc <- sagemakeredgemanager() svc$get_deployments( Foo = 123 ) ## End(Not run)
Contains all data plane API operations and data types for the Amazon SageMaker Feature Store. Use this API to put, delete, and retrieve (get) features from a feature store.
Use the following operations to configure your OnlineStore and
OfflineStore features, and to create and manage feature groups:
sagemakerfeaturestoreruntime( config = list(), credentials = list(), endpoint = NULL, region = NULL )sagemakerfeaturestoreruntime( config = list(), credentials = list(), endpoint = NULL, region = NULL )
config |
Optional configuration of credentials, endpoint, and/or region.
|
credentials |
Optional credentials shorthand for the config parameter
|
endpoint |
Optional shorthand for complete URL to use for the constructed client. |
region |
Optional shorthand for AWS Region used in instantiating the client. |
A client for the service. You can call the service's operations using
syntax like svc$operation(...), where svc is the name you've assigned
to the client. The available operations are listed in the
Operations section.
svc <- sagemakerfeaturestoreruntime(
config = list(
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string",
close_connection = "logical",
timeout = "numeric",
s3_force_path_style = "logical",
sts_regional_endpoint = "string"
),
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string"
)
| batch_get_record | Retrieves a batch of Records from a FeatureGroup |
| delete_record | Deletes a Record from a FeatureGroup in the OnlineStore |
| get_record | Use for OnlineStore serving from a FeatureStore |
| put_record | The PutRecord API is used to ingest a list of Records into your feature group |
## Not run: svc <- sagemakerfeaturestoreruntime() svc$batch_get_record( Foo = 123 ) ## End(Not run)## Not run: svc <- sagemakerfeaturestoreruntime() svc$batch_get_record( Foo = 123 ) ## End(Not run)
Provides APIs for creating and managing SageMaker geospatial resources.
sagemakergeospatialcapabilities( config = list(), credentials = list(), endpoint = NULL, region = NULL )sagemakergeospatialcapabilities( config = list(), credentials = list(), endpoint = NULL, region = NULL )
config |
Optional configuration of credentials, endpoint, and/or region.
|
credentials |
Optional credentials shorthand for the config parameter
|
endpoint |
Optional shorthand for complete URL to use for the constructed client. |
region |
Optional shorthand for AWS Region used in instantiating the client. |
A client for the service. You can call the service's operations using
syntax like svc$operation(...), where svc is the name you've assigned
to the client. The available operations are listed in the
Operations section.
svc <- sagemakergeospatialcapabilities(
config = list(
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string",
close_connection = "logical",
timeout = "numeric",
s3_force_path_style = "logical",
sts_regional_endpoint = "string"
),
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string"
)
| delete_earth_observation_job | Use this operation to delete an Earth Observation job |
| delete_vector_enrichment_job | Use this operation to delete a Vector Enrichment job |
| export_earth_observation_job | Use this operation to export results of an Earth Observation job and optionally source images used as input to the EOJ to an Amazon S3 location |
| export_vector_enrichment_job | Use this operation to copy results of a Vector Enrichment job to an Amazon S3 location |
| get_earth_observation_job | Get the details for a previously initiated Earth Observation job |
| get_raster_data_collection | Use this operation to get details of a specific raster data collection |
| get_tile | Gets a web mercator tile for the given Earth Observation job |
| get_vector_enrichment_job | Retrieves details of a Vector Enrichment Job for a given job Amazon Resource Name (ARN) |
| list_earth_observation_jobs | Use this operation to get a list of the Earth Observation jobs associated with the calling Amazon Web Services account |
| list_raster_data_collections | Use this operation to get raster data collections |
| list_tags_for_resource | Lists the tags attached to the resource |
| list_vector_enrichment_jobs | Retrieves a list of vector enrichment jobs |
| search_raster_data_collection | Allows you run image query on a specific raster data collection to get a list of the satellite imagery matching the selected filters |
| start_earth_observation_job | Use this operation to create an Earth observation job |
| start_vector_enrichment_job | Creates a Vector Enrichment job for the supplied job type |
| stop_earth_observation_job | Use this operation to stop an existing earth observation job |
| stop_vector_enrichment_job | Stops the Vector Enrichment job for a given job ARN |
| tag_resource | The resource you want to tag |
| untag_resource | The resource you want to untag |
## Not run: svc <- sagemakergeospatialcapabilities() svc$delete_earth_observation_job( Foo = 123 ) ## End(Not run)## Not run: svc <- sagemakergeospatialcapabilities() svc$delete_earth_observation_job( Foo = 123 ) ## End(Not run)
Contains all data plane API operations and data types for Amazon SageMaker Metrics. Use these APIs to put and retrieve (get) features related to your training run.
sagemakermetrics( config = list(), credentials = list(), endpoint = NULL, region = NULL )sagemakermetrics( config = list(), credentials = list(), endpoint = NULL, region = NULL )
config |
Optional configuration of credentials, endpoint, and/or region.
|
credentials |
Optional credentials shorthand for the config parameter
|
endpoint |
Optional shorthand for complete URL to use for the constructed client. |
region |
Optional shorthand for AWS Region used in instantiating the client. |
A client for the service. You can call the service's operations using
syntax like svc$operation(...), where svc is the name you've assigned
to the client. The available operations are listed in the
Operations section.
svc <- sagemakermetrics(
config = list(
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string",
close_connection = "logical",
timeout = "numeric",
s3_force_path_style = "logical",
sts_regional_endpoint = "string"
),
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string"
)
| batch_put_metrics | Used to ingest training metrics into SageMaker |
## Not run: svc <- sagemakermetrics() svc$batch_put_metrics( Foo = 123 ) ## End(Not run)## Not run: svc <- sagemakermetrics() svc$batch_put_metrics( Foo = 123 ) ## End(Not run)
The Amazon SageMaker runtime API.
sagemakerruntime( config = list(), credentials = list(), endpoint = NULL, region = NULL )sagemakerruntime( config = list(), credentials = list(), endpoint = NULL, region = NULL )
config |
Optional configuration of credentials, endpoint, and/or region.
|
credentials |
Optional credentials shorthand for the config parameter
|
endpoint |
Optional shorthand for complete URL to use for the constructed client. |
region |
Optional shorthand for AWS Region used in instantiating the client. |
A client for the service. You can call the service's operations using
syntax like svc$operation(...), where svc is the name you've assigned
to the client. The available operations are listed in the
Operations section.
svc <- sagemakerruntime(
config = list(
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string",
close_connection = "logical",
timeout = "numeric",
s3_force_path_style = "logical",
sts_regional_endpoint = "string"
),
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string"
)
| invoke_endpoint | After you deploy a model into production using Amazon SageMaker hosting services, your client applications use this API to get inferences from the model hosted at the specified endpoint |
| invoke_endpoint_async | After you deploy a model into production using Amazon SageMaker hosting services, your client applications use this API to get inferences from the model hosted at the specified endpoint in an asynchronous manner |
| invoke_endpoint_with_response_stream | Invokes a model at the specified endpoint to return the inference response as a stream |
## Not run: svc <- sagemakerruntime() svc$invoke_endpoint( Foo = 123 ) ## End(Not run)## Not run: svc <- sagemakerruntime() svc$invoke_endpoint( Foo = 123 ) ## End(Not run)
Amazon Textract detects and analyzes text in documents and converts it into machine-readable text. This is the API reference documentation for Amazon Textract.
textract(config = list(), credentials = list(), endpoint = NULL, region = NULL)textract(config = list(), credentials = list(), endpoint = NULL, region = NULL)
config |
Optional configuration of credentials, endpoint, and/or region.
|
credentials |
Optional credentials shorthand for the config parameter
|
endpoint |
Optional shorthand for complete URL to use for the constructed client. |
region |
Optional shorthand for AWS Region used in instantiating the client. |
A client for the service. You can call the service's operations using
syntax like svc$operation(...), where svc is the name you've assigned
to the client. The available operations are listed in the
Operations section.
svc <- textract(
config = list(
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string",
close_connection = "logical",
timeout = "numeric",
s3_force_path_style = "logical",
sts_regional_endpoint = "string"
),
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string"
)
| analyze_document | Analyzes an input document for relationships between detected items |
| analyze_expense | AnalyzeExpense synchronously analyzes an input document for financially related relationships between text |
| analyze_id | Analyzes identity documents for relevant information |
| create_adapter | Creates an adapter, which can be fine-tuned for enhanced performance on user provided documents |
| create_adapter_version | Creates a new version of an adapter |
| delete_adapter | Deletes an Amazon Textract adapter |
| delete_adapter_version | Deletes an Amazon Textract adapter version |
| detect_document_text | Detects text in the input document |
| get_adapter | Gets configuration information for an adapter specified by an AdapterId, returning information on AdapterName, Description, CreationTime, AutoUpdate status, and FeatureTypes |
| get_adapter_version | Gets configuration information for the specified adapter version, including: AdapterId, AdapterVersion, FeatureTypes, Status, StatusMessage, DatasetConfig, KMSKeyId, OutputConfig, Tags and EvaluationMetrics |
| get_document_analysis | Gets the results for an Amazon Textract asynchronous operation that analyzes text in a document |
| get_document_text_detection | Gets the results for an Amazon Textract asynchronous operation that detects text in a document |
| get_expense_analysis | Gets the results for an Amazon Textract asynchronous operation that analyzes invoices and receipts |
| get_lending_analysis | Gets the results for an Amazon Textract asynchronous operation that analyzes text in a lending document |
| get_lending_analysis_summary | Gets summarized results for the StartLendingAnalysis operation, which analyzes text in a lending document |
| list_adapters | Lists all adapters that match the specified filtration criteria |
| list_adapter_versions | List all version of an adapter that meet the specified filtration criteria |
| list_tags_for_resource | Lists all tags for an Amazon Textract resource |
| start_document_analysis | Starts the asynchronous analysis of an input document for relationships between detected items such as key-value pairs, tables, and selection elements |
| start_document_text_detection | Starts the asynchronous detection of text in a document |
| start_expense_analysis | Starts the asynchronous analysis of invoices or receipts for data like contact information, items purchased, and vendor names |
| start_lending_analysis | Starts the classification and analysis of an input document |
| tag_resource | Adds one or more tags to the specified resource |
| untag_resource | Removes any tags with the specified keys from the specified resource |
| update_adapter | Update the configuration for an adapter |
## Not run: svc <- textract() svc$analyze_document( Foo = 123 ) ## End(Not run)## Not run: svc <- textract() svc$analyze_document( Foo = 123 ) ## End(Not run)
Amazon Transcribe offers three main types of batch transcription: Standard, Medical, and Call Analytics.
Standard transcriptions are the most common option. Refer to for details.
Medical transcriptions are tailored to medical professionals and incorporate medical terms. A common use case for this service is transcribing doctor-patient dialogue into after-visit notes. Refer to for details.
Call Analytics transcriptions are designed for use with call center audio on two different channels; if you're looking for insight into customer service calls, use this option. Refer to for details.
transcribeservice( config = list(), credentials = list(), endpoint = NULL, region = NULL )transcribeservice( config = list(), credentials = list(), endpoint = NULL, region = NULL )
config |
Optional configuration of credentials, endpoint, and/or region.
|
credentials |
Optional credentials shorthand for the config parameter
|
endpoint |
Optional shorthand for complete URL to use for the constructed client. |
region |
Optional shorthand for AWS Region used in instantiating the client. |
A client for the service. You can call the service's operations using
syntax like svc$operation(...), where svc is the name you've assigned
to the client. The available operations are listed in the
Operations section.
svc <- transcribeservice(
config = list(
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string",
close_connection = "logical",
timeout = "numeric",
s3_force_path_style = "logical",
sts_regional_endpoint = "string"
),
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string"
)
| create_call_analytics_category | Creates a new Call Analytics category |
| create_language_model | Creates a new custom language model |
| create_medical_vocabulary | Creates a new custom medical vocabulary |
| create_vocabulary | Creates a new custom vocabulary |
| create_vocabulary_filter | Creates a new custom vocabulary filter |
| delete_call_analytics_category | Deletes a Call Analytics category |
| delete_call_analytics_job | Deletes a Call Analytics job |
| delete_language_model | Deletes a custom language model |
| delete_medical_scribe_job | Deletes a Medical Scribe job |
| delete_medical_transcription_job | Deletes a medical transcription job |
| delete_medical_vocabulary | Deletes a custom medical vocabulary |
| delete_transcription_job | Deletes a transcription job |
| delete_vocabulary | Deletes a custom vocabulary |
| delete_vocabulary_filter | Deletes a custom vocabulary filter |
| describe_language_model | Provides information about the specified custom language model |
| get_call_analytics_category | Provides information about the specified Call Analytics category |
| get_call_analytics_job | Provides information about the specified Call Analytics job |
| get_medical_scribe_job | Provides information about the specified Medical Scribe job |
| get_medical_transcription_job | Provides information about the specified medical transcription job |
| get_medical_vocabulary | Provides information about the specified custom medical vocabulary |
| get_transcription_job | Provides information about the specified transcription job |
| get_vocabulary | Provides information about the specified custom vocabulary |
| get_vocabulary_filter | Provides information about the specified custom vocabulary filter |
| list_call_analytics_categories | Provides a list of Call Analytics categories, including all rules that make up each category |
| list_call_analytics_jobs | Provides a list of Call Analytics jobs that match the specified criteria |
| list_language_models | Provides a list of custom language models that match the specified criteria |
| list_medical_scribe_jobs | Provides a list of Medical Scribe jobs that match the specified criteria |
| list_medical_transcription_jobs | Provides a list of medical transcription jobs that match the specified criteria |
| list_medical_vocabularies | Provides a list of custom medical vocabularies that match the specified criteria |
| list_tags_for_resource | Lists all tags associated with the specified transcription job, vocabulary, model, or resource |
| list_transcription_jobs | Provides a list of transcription jobs that match the specified criteria |
| list_vocabularies | Provides a list of custom vocabularies that match the specified criteria |
| list_vocabulary_filters | Provides a list of custom vocabulary filters that match the specified criteria |
| start_call_analytics_job | Transcribes the audio from a customer service call and applies any additional Request Parameters you choose to include in your request |
| start_medical_scribe_job | Transcribes patient-clinician conversations and generates clinical notes |
| start_medical_transcription_job | Transcribes the audio from a medical dictation or conversation and applies any additional Request Parameters you choose to include in your request |
| start_transcription_job | Transcribes the audio from a media file and applies any additional Request Parameters you choose to include in your request |
| tag_resource | Adds one or more custom tags, each in the form of a key:value pair, to the specified resource |
| untag_resource | Removes the specified tags from the specified Amazon Transcribe resource |
| update_call_analytics_category | Updates the specified Call Analytics category with new rules |
| update_medical_vocabulary | Updates an existing custom medical vocabulary with new values |
| update_vocabulary | Updates an existing custom vocabulary with new values |
| update_vocabulary_filter | Updates an existing custom vocabulary filter with a new list of words |
## Not run: svc <- transcribeservice() svc$create_call_analytics_category( Foo = 123 ) ## End(Not run)## Not run: svc <- transcribeservice() svc$create_call_analytics_category( Foo = 123 ) ## End(Not run)
Provides translation of the input content from the source language to the target language.
translate( config = list(), credentials = list(), endpoint = NULL, region = NULL )translate( config = list(), credentials = list(), endpoint = NULL, region = NULL )
config |
Optional configuration of credentials, endpoint, and/or region.
|
credentials |
Optional credentials shorthand for the config parameter
|
endpoint |
Optional shorthand for complete URL to use for the constructed client. |
region |
Optional shorthand for AWS Region used in instantiating the client. |
A client for the service. You can call the service's operations using
syntax like svc$operation(...), where svc is the name you've assigned
to the client. The available operations are listed in the
Operations section.
svc <- translate(
config = list(
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string",
close_connection = "logical",
timeout = "numeric",
s3_force_path_style = "logical",
sts_regional_endpoint = "string"
),
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string"
)
| create_parallel_data | Creates a parallel data resource in Amazon Translate by importing an input file from Amazon S3 |
| delete_parallel_data | Deletes a parallel data resource in Amazon Translate |
| delete_terminology | A synchronous action that deletes a custom terminology |
| describe_text_translation_job | Gets the properties associated with an asynchronous batch translation job including name, ID, status, source and target languages, input/output S3 buckets, and so on |
| get_parallel_data | Provides information about a parallel data resource |
| get_terminology | Retrieves a custom terminology |
| import_terminology | Creates or updates a custom terminology, depending on whether one already exists for the given terminology name |
| list_languages | Provides a list of languages (RFC-5646 codes and names) that Amazon Translate supports |
| list_parallel_data | Provides a list of your parallel data resources in Amazon Translate |
| list_tags_for_resource | Lists all tags associated with a given Amazon Translate resource |
| list_terminologies | Provides a list of custom terminologies associated with your account |
| list_text_translation_jobs | Gets a list of the batch translation jobs that you have submitted |
| start_text_translation_job | Starts an asynchronous batch translation job |
| stop_text_translation_job | Stops an asynchronous batch translation job that is in progress |
| tag_resource | Associates a specific tag with a resource |
| translate_document | Translates the input document from the source language to the target language |
| translate_text | Translates input text from the source language to the target language |
| untag_resource | Removes a specific tag associated with an Amazon Translate resource |
| update_parallel_data | Updates a previously created parallel data resource by importing a new input file from Amazon S3 |
## Not run: svc <- translate() svc$create_parallel_data( Foo = 123 ) ## End(Not run)## Not run: svc <- translate() svc$create_parallel_data( Foo = 123 ) ## End(Not run)
Amazon Connect Voice ID provides real-time caller authentication and fraud risk detection, which make voice interactions in contact centers more secure and efficient.
voiceid(config = list(), credentials = list(), endpoint = NULL, region = NULL)voiceid(config = list(), credentials = list(), endpoint = NULL, region = NULL)
config |
Optional configuration of credentials, endpoint, and/or region.
|
credentials |
Optional credentials shorthand for the config parameter
|
endpoint |
Optional shorthand for complete URL to use for the constructed client. |
region |
Optional shorthand for AWS Region used in instantiating the client. |
A client for the service. You can call the service's operations using
syntax like svc$operation(...), where svc is the name you've assigned
to the client. The available operations are listed in the
Operations section.
svc <- voiceid(
config = list(
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string",
close_connection = "logical",
timeout = "numeric",
s3_force_path_style = "logical",
sts_regional_endpoint = "string"
),
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string"
)
| associate_fraudster | Associates the fraudsters with the watchlist specified in the same domain |
| create_domain | Creates a domain that contains all Amazon Connect Voice ID data, such as speakers, fraudsters, customer audio, and voiceprints |
| create_watchlist | Creates a watchlist that fraudsters can be a part of |
| delete_domain | Deletes the specified domain from Voice ID |
| delete_fraudster | Deletes the specified fraudster from Voice ID |
| delete_speaker | Deletes the specified speaker from Voice ID |
| delete_watchlist | Deletes the specified watchlist from Voice ID |
| describe_domain | Describes the specified domain |
| describe_fraudster | Describes the specified fraudster |
| describe_fraudster_registration_job | Describes the specified fraudster registration job |
| describe_speaker | Describes the specified speaker |
| describe_speaker_enrollment_job | Describes the specified speaker enrollment job |
| describe_watchlist | Describes the specified watchlist |
| disassociate_fraudster | Disassociates the fraudsters from the watchlist specified |
| evaluate_session | Evaluates a specified session based on audio data accumulated during a streaming Amazon Connect Voice ID call |
| list_domains | Lists all the domains in the Amazon Web Services account |
| list_fraudster_registration_jobs | Lists all the fraudster registration jobs in the domain with the given JobStatus |
| list_fraudsters | Lists all fraudsters in a specified watchlist or domain |
| list_speaker_enrollment_jobs | Lists all the speaker enrollment jobs in the domain with the specified JobStatus |
| list_speakers | Lists all speakers in a specified domain |
| list_tags_for_resource | Lists all tags associated with a specified Voice ID resource |
| list_watchlists | Lists all watchlists in a specified domain |
| opt_out_speaker | Opts out a speaker from Voice ID |
| start_fraudster_registration_job | Starts a new batch fraudster registration job using provided details |
| start_speaker_enrollment_job | Starts a new batch speaker enrollment job using specified details |
| tag_resource | Tags a Voice ID resource with the provided list of tags |
| untag_resource | Removes specified tags from a specified Amazon Connect Voice ID resource |
| update_domain | Updates the specified domain |
| update_watchlist | Updates the specified watchlist |
## Not run: svc <- voiceid() svc$associate_fraudster( Foo = 123 ) ## End(Not run)## Not run: svc <- voiceid() svc$associate_fraudster( Foo = 123 ) ## End(Not run)