prowler-sdk-poc/node_modules/aws-sdk/clients/sagemaker.d.ts

# Python

import {Request} from '../lib/request';
import {Response} from '../lib/response';
import {AWSError} from '../lib/error';
import {Service} from '../lib/service';
import {WaiterConfiguration} from '../lib/service';
import {ServiceConfigurationOptions} from '../lib/service';
import {ConfigBase as Config} from '../lib/config-base';
interface Blob {}
declare class SageMaker extends Service {
  /**
   * Constructs a service object. This object has one method for each API operation.
   */
  constructor(options?: SageMaker.Types.ClientConfiguration)
  config: Config & SageMaker.Types.ClientConfiguration;
  /**
   * Creates an association between the source and the destination. A source can be associated with multiple destinations, and a destination can be associated with multiple sources. An association is a lineage tracking entity. For more information, see Amazon SageMaker ML Lineage Tracking.
   */
  addAssociation(params: SageMaker.Types.AddAssociationRequest, callback?: (err: AWSError, data: SageMaker.Types.AddAssociationResponse) => void): Request<SageMaker.Types.AddAssociationResponse, AWSError>;
  /**
   * Creates an association between the source and the destination. A source can be associated with multiple destinations, and a destination can be associated with multiple sources. An association is a lineage tracking entity. For more information, see Amazon SageMaker ML Lineage Tracking.
   */
  addAssociation(callback?: (err: AWSError, data: SageMaker.Types.AddAssociationResponse) => void): Request<SageMaker.Types.AddAssociationResponse, AWSError>;
  /**
   * Adds or overwrites one or more tags for the specified Amazon SageMaker resource. You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. Each tag consists of a key and an optional value. Tag keys must be unique per resource. For more information about tags, see For more information, see Amazon Web Services Tagging Strategies.  Tags that you add to a hyperparameter tuning job by calling this API are also added to any training jobs that the hyperparameter tuning job launches after you call this API, but not to training jobs that the hyperparameter tuning job launched before you called this API. To make sure that the tags associated with a hyperparameter tuning job are also added to all training jobs that the hyperparameter tuning job launches, add the tags when you first create the tuning job by specifying them in the Tags parameter of CreateHyperParameterTuningJob    Tags that you add to a SageMaker Studio Domain or User Profile by calling this API are also added to any Apps that the Domain or User Profile launches after you call this API, but not to Apps that the Domain or User Profile launched before you called this API. To make sure that the tags associated with a Domain or User Profile are also added to all Apps that the Domain or User Profile launches, add the tags when you first create the Domain or User Profile by specifying them in the Tags parameter of CreateDomain or CreateUserProfile. 
   */
  addTags(params: SageMaker.Types.AddTagsInput, callback?: (err: AWSError, data: SageMaker.Types.AddTagsOutput) => void): Request<SageMaker.Types.AddTagsOutput, AWSError>;
  /**
   * Adds or overwrites one or more tags for the specified Amazon SageMaker resource. You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. Each tag consists of a key and an optional value. Tag keys must be unique per resource. For more information about tags, see For more information, see Amazon Web Services Tagging Strategies.  Tags that you add to a hyperparameter tuning job by calling this API are also added to any training jobs that the hyperparameter tuning job launches after you call this API, but not to training jobs that the hyperparameter tuning job launched before you called this API. To make sure that the tags associated with a hyperparameter tuning job are also added to all training jobs that the hyperparameter tuning job launches, add the tags when you first create the tuning job by specifying them in the Tags parameter of CreateHyperParameterTuningJob    Tags that you add to a SageMaker Studio Domain or User Profile by calling this API are also added to any Apps that the Domain or User Profile launches after you call this API, but not to Apps that the Domain or User Profile launched before you called this API. To make sure that the tags associated with a Domain or User Profile are also added to all Apps that the Domain or User Profile launches, add the tags when you first create the Domain or User Profile by specifying them in the Tags parameter of CreateDomain or CreateUserProfile. 
   */
  addTags(callback?: (err: AWSError, data: SageMaker.Types.AddTagsOutput) => void): Request<SageMaker.Types.AddTagsOutput, AWSError>;
  /**
   * Associates a trial component with a trial. A trial component can be associated with multiple trials. To disassociate a trial component from a trial, call the DisassociateTrialComponent API.
   */
  associateTrialComponent(params: SageMaker.Types.AssociateTrialComponentRequest, callback?: (err: AWSError, data: SageMaker.Types.AssociateTrialComponentResponse) => void): Request<SageMaker.Types.AssociateTrialComponentResponse, AWSError>;
  /**
   * Associates a trial component with a trial. A trial component can be associated with multiple trials. To disassociate a trial component from a trial, call the DisassociateTrialComponent API.
   */
  associateTrialComponent(callback?: (err: AWSError, data: SageMaker.Types.AssociateTrialComponentResponse) => void): Request<SageMaker.Types.AssociateTrialComponentResponse, AWSError>;
  /**
   * Creates an action. An action is a lineage tracking entity that represents an action or activity. For example, a model deployment or an HPO job. Generally, an action involves at least one input or output artifact. For more information, see Amazon SageMaker ML Lineage Tracking.
   */
  createAction(params: SageMaker.Types.CreateActionRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateActionResponse) => void): Request<SageMaker.Types.CreateActionResponse, AWSError>;
  /**
   * Creates an action. An action is a lineage tracking entity that represents an action or activity. For example, a model deployment or an HPO job. Generally, an action involves at least one input or output artifact. For more information, see Amazon SageMaker ML Lineage Tracking.
   */
  createAction(callback?: (err: AWSError, data: SageMaker.Types.CreateActionResponse) => void): Request<SageMaker.Types.CreateActionResponse, AWSError>;
  /**
   * Create a machine learning algorithm that you can use in Amazon SageMaker and list in the Amazon Web Services Marketplace.
   */
  createAlgorithm(params: SageMaker.Types.CreateAlgorithmInput, callback?: (err: AWSError, data: SageMaker.Types.CreateAlgorithmOutput) => void): Request<SageMaker.Types.CreateAlgorithmOutput, AWSError>;
  /**
   * Create a machine learning algorithm that you can use in Amazon SageMaker and list in the Amazon Web Services Marketplace.
   */
  createAlgorithm(callback?: (err: AWSError, data: SageMaker.Types.CreateAlgorithmOutput) => void): Request<SageMaker.Types.CreateAlgorithmOutput, AWSError>;
  /**
   * Creates a running app for the specified UserProfile. Supported apps are JupyterServer and KernelGateway. This operation is automatically invoked by Amazon SageMaker Studio upon access to the associated Domain, and when new kernel configurations are selected by the user. A user may have multiple Apps active simultaneously.
   */
  createApp(params: SageMaker.Types.CreateAppRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateAppResponse) => void): Request<SageMaker.Types.CreateAppResponse, AWSError>;
  /**
   * Creates a running app for the specified UserProfile. Supported apps are JupyterServer and KernelGateway. This operation is automatically invoked by Amazon SageMaker Studio upon access to the associated Domain, and when new kernel configurations are selected by the user. A user may have multiple Apps active simultaneously.
   */
  createApp(callback?: (err: AWSError, data: SageMaker.Types.CreateAppResponse) => void): Request<SageMaker.Types.CreateAppResponse, AWSError>;
  /**
   * Creates a configuration for running a SageMaker image as a KernelGateway app. The configuration specifies the Amazon Elastic File System (EFS) storage volume on the image, and a list of the kernels in the image.
   */
  createAppImageConfig(params: SageMaker.Types.CreateAppImageConfigRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateAppImageConfigResponse) => void): Request<SageMaker.Types.CreateAppImageConfigResponse, AWSError>;
  /**
   * Creates a configuration for running a SageMaker image as a KernelGateway app. The configuration specifies the Amazon Elastic File System (EFS) storage volume on the image, and a list of the kernels in the image.
   */
  createAppImageConfig(callback?: (err: AWSError, data: SageMaker.Types.CreateAppImageConfigResponse) => void): Request<SageMaker.Types.CreateAppImageConfigResponse, AWSError>;
  /**
   * Creates an artifact. An artifact is a lineage tracking entity that represents a URI addressable object or data. Some examples are the S3 URI of a dataset and the ECR registry path of an image. For more information, see Amazon SageMaker ML Lineage Tracking.
   */
  createArtifact(params: SageMaker.Types.CreateArtifactRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateArtifactResponse) => void): Request<SageMaker.Types.CreateArtifactResponse, AWSError>;
  /**
   * Creates an artifact. An artifact is a lineage tracking entity that represents a URI addressable object or data. Some examples are the S3 URI of a dataset and the ECR registry path of an image. For more information, see Amazon SageMaker ML Lineage Tracking.
   */
  createArtifact(callback?: (err: AWSError, data: SageMaker.Types.CreateArtifactResponse) => void): Request<SageMaker.Types.CreateArtifactResponse, AWSError>;
  /**
   * Creates an Autopilot job. Find the best-performing model after you run an Autopilot job by calling . For information about how to use Autopilot, see Automate Model Development with Amazon SageMaker Autopilot.
   */
  createAutoMLJob(params: SageMaker.Types.CreateAutoMLJobRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateAutoMLJobResponse) => void): Request<SageMaker.Types.CreateAutoMLJobResponse, AWSError>;
  /**
   * Creates an Autopilot job. Find the best-performing model after you run an Autopilot job by calling . For information about how to use Autopilot, see Automate Model Development with Amazon SageMaker Autopilot.
   */
  createAutoMLJob(callback?: (err: AWSError, data: SageMaker.Types.CreateAutoMLJobResponse) => void): Request<SageMaker.Types.CreateAutoMLJobResponse, AWSError>;
  /**
   * Creates a Git repository as a resource in your Amazon SageMaker account. You can associate the repository with notebook instances so that you can use Git source control for the notebooks you create. The Git repository is a resource in your Amazon SageMaker account, so it can be associated with more than one notebook instance, and it persists independently from the lifecycle of any notebook instances it is associated with. The repository can be hosted either in Amazon Web Services CodeCommit or in any other Git repository.
   */
  createCodeRepository(params: SageMaker.Types.CreateCodeRepositoryInput, callback?: (err: AWSError, data: SageMaker.Types.CreateCodeRepositoryOutput) => void): Request<SageMaker.Types.CreateCodeRepositoryOutput, AWSError>;
  /**
   * Creates a Git repository as a resource in your Amazon SageMaker account. You can associate the repository with notebook instances so that you can use Git source control for the notebooks you create. The Git repository is a resource in your Amazon SageMaker account, so it can be associated with more than one notebook instance, and it persists independently from the lifecycle of any notebook instances it is associated with. The repository can be hosted either in Amazon Web Services CodeCommit or in any other Git repository.
   */
  createCodeRepository(callback?: (err: AWSError, data: SageMaker.Types.CreateCodeRepositoryOutput) => void): Request<SageMaker.Types.CreateCodeRepositoryOutput, AWSError>;
  /**
   * Starts a model compilation job. After the model has been compiled, Amazon SageMaker saves the resulting model artifacts to an Amazon Simple Storage Service (Amazon S3) bucket that you specify.  If you choose to host your model using Amazon SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts with Amazon Web Services IoT Greengrass. In that case, deploy them as an ML resource. In the request body, you provide the following:   A name for the compilation job    Information about the input model artifacts    The output location for the compiled model and the device (target) that the model runs on    The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker assumes to perform the model compilation job.    You can also provide a Tag to track the model compilation job's resource use and costs. The response body contains the CompilationJobArn for the compiled job. To stop a model compilation job, use StopCompilationJob. To get information about a particular model compilation job, use DescribeCompilationJob. To get information about multiple model compilation jobs, use ListCompilationJobs.
   */
  createCompilationJob(params: SageMaker.Types.CreateCompilationJobRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateCompilationJobResponse) => void): Request<SageMaker.Types.CreateCompilationJobResponse, AWSError>;
  /**
   * Starts a model compilation job. After the model has been compiled, Amazon SageMaker saves the resulting model artifacts to an Amazon Simple Storage Service (Amazon S3) bucket that you specify.  If you choose to host your model using Amazon SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts with Amazon Web Services IoT Greengrass. In that case, deploy them as an ML resource. In the request body, you provide the following:   A name for the compilation job    Information about the input model artifacts    The output location for the compiled model and the device (target) that the model runs on    The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker assumes to perform the model compilation job.    You can also provide a Tag to track the model compilation job's resource use and costs. The response body contains the CompilationJobArn for the compiled job. To stop a model compilation job, use StopCompilationJob. To get information about a particular model compilation job, use DescribeCompilationJob. To get information about multiple model compilation jobs, use ListCompilationJobs.
   */
  createCompilationJob(callback?: (err: AWSError, data: SageMaker.Types.CreateCompilationJobResponse) => void): Request<SageMaker.Types.CreateCompilationJobResponse, AWSError>;
  /**
   * Creates a context. A context is a lineage tracking entity that represents a logical grouping of other tracking or experiment entities. Some examples are an endpoint and a model package. For more information, see Amazon SageMaker ML Lineage Tracking.
   */
  createContext(params: SageMaker.Types.CreateContextRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateContextResponse) => void): Request<SageMaker.Types.CreateContextResponse, AWSError>;
  /**
   * Creates a context. A context is a lineage tracking entity that represents a logical grouping of other tracking or experiment entities. Some examples are an endpoint and a model package. For more information, see Amazon SageMaker ML Lineage Tracking.
   */
  createContext(callback?: (err: AWSError, data: SageMaker.Types.CreateContextResponse) => void): Request<SageMaker.Types.CreateContextResponse, AWSError>;
  /**
   * Creates a definition for a job that monitors data quality and drift. For information about model monitor, see Amazon SageMaker Model Monitor.
   */
  createDataQualityJobDefinition(params: SageMaker.Types.CreateDataQualityJobDefinitionRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateDataQualityJobDefinitionResponse) => void): Request<SageMaker.Types.CreateDataQualityJobDefinitionResponse, AWSError>;
  /**
   * Creates a definition for a job that monitors data quality and drift. For information about model monitor, see Amazon SageMaker Model Monitor.
   */
  createDataQualityJobDefinition(callback?: (err: AWSError, data: SageMaker.Types.CreateDataQualityJobDefinitionResponse) => void): Request<SageMaker.Types.CreateDataQualityJobDefinitionResponse, AWSError>;
  /**
   * Creates a device fleet.
   */
  createDeviceFleet(params: SageMaker.Types.CreateDeviceFleetRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Creates a device fleet.
   */
  createDeviceFleet(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Creates a Domain used by Amazon SageMaker Studio. A domain consists of an associated Amazon Elastic File System (EFS) volume, a list of authorized users, and a variety of security, application, policy, and Amazon Virtual Private Cloud (VPC) configurations. An Amazon Web Services account is limited to one domain per region. Users within a domain can share notebook files and other artifacts with each other.  EFS storage  When a domain is created, an EFS volume is created for use by all of the users within the domain. Each user receives a private home directory within the EFS volume for notebooks, Git repositories, and data files. SageMaker uses the Amazon Web Services Key Management Service (Amazon Web Services KMS) to encrypt the EFS volume attached to the domain with an Amazon Web Services managed key by default. For more control, you can specify a customer managed key. For more information, see Protect Data at Rest Using Encryption.  VPC configuration  All SageMaker Studio traffic between the domain and the EFS volume is through the specified VPC and subnets. For other Studio traffic, you can specify the AppNetworkAccessType parameter. AppNetworkAccessType corresponds to the network access type that you choose when you onboard to Studio. The following options are available:    PublicInternetOnly - Non-EFS traffic goes through a VPC managed by Amazon SageMaker, which allows internet access. This is the default value.    VpcOnly - All Studio traffic is through the specified VPC and subnets. Internet access is disabled by default. To allow internet access, you must specify a NAT gateway. When internet access is disabled, you won't be able to run a Studio notebook or to train or host models unless your VPC has an interface endpoint to the SageMaker API and runtime or a NAT gateway and your security groups allow outbound connections.    NFS traffic over TCP on port 2049 needs to be allowed in both inbound and outbound rules in order to launch a SageMaker Studio app successfully.  For more information, see Connect SageMaker Studio Notebooks to Resources in a VPC.
   */
  createDomain(params: SageMaker.Types.CreateDomainRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateDomainResponse) => void): Request<SageMaker.Types.CreateDomainResponse, AWSError>;
  /**
   * Creates a Domain used by Amazon SageMaker Studio. A domain consists of an associated Amazon Elastic File System (EFS) volume, a list of authorized users, and a variety of security, application, policy, and Amazon Virtual Private Cloud (VPC) configurations. An Amazon Web Services account is limited to one domain per region. Users within a domain can share notebook files and other artifacts with each other.  EFS storage  When a domain is created, an EFS volume is created for use by all of the users within the domain. Each user receives a private home directory within the EFS volume for notebooks, Git repositories, and data files. SageMaker uses the Amazon Web Services Key Management Service (Amazon Web Services KMS) to encrypt the EFS volume attached to the domain with an Amazon Web Services managed key by default. For more control, you can specify a customer managed key. For more information, see Protect Data at Rest Using Encryption.  VPC configuration  All SageMaker Studio traffic between the domain and the EFS volume is through the specified VPC and subnets. For other Studio traffic, you can specify the AppNetworkAccessType parameter. AppNetworkAccessType corresponds to the network access type that you choose when you onboard to Studio. The following options are available:    PublicInternetOnly - Non-EFS traffic goes through a VPC managed by Amazon SageMaker, which allows internet access. This is the default value.    VpcOnly - All Studio traffic is through the specified VPC and subnets. Internet access is disabled by default. To allow internet access, you must specify a NAT gateway. When internet access is disabled, you won't be able to run a Studio notebook or to train or host models unless your VPC has an interface endpoint to the SageMaker API and runtime or a NAT gateway and your security groups allow outbound connections.    NFS traffic over TCP on port 2049 needs to be allowed in both inbound and outbound rules in order to launch a SageMaker Studio app successfully.  For more information, see Connect SageMaker Studio Notebooks to Resources in a VPC.
   */
  createDomain(callback?: (err: AWSError, data: SageMaker.Types.CreateDomainResponse) => void): Request<SageMaker.Types.CreateDomainResponse, AWSError>;
  /**
   * Starts a SageMaker Edge Manager model packaging job. Edge Manager will use the model artifacts from the Amazon Simple Storage Service bucket that you specify. After the model has been packaged, Amazon SageMaker saves the resulting artifacts to an S3 bucket that you specify.
   */
  createEdgePackagingJob(params: SageMaker.Types.CreateEdgePackagingJobRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Starts a SageMaker Edge Manager model packaging job. Edge Manager will use the model artifacts from the Amazon Simple Storage Service bucket that you specify. After the model has been packaged, Amazon SageMaker saves the resulting artifacts to an S3 bucket that you specify.
   */
  createEdgePackagingJob(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Creates an endpoint using the endpoint configuration specified in the request. Amazon SageMaker uses the endpoint to provision resources and deploy models. You create the endpoint configuration with the CreateEndpointConfig API.   Use this API to deploy models using Amazon SageMaker hosting services.  For an example that calls this method when deploying a model to Amazon SageMaker hosting services, see the Create Endpoint example notebook.    You must not delete an EndpointConfig that is in use by an endpoint that is live or while the UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. To update an endpoint, you must create a new EndpointConfig.  The endpoint name must be unique within an Amazon Web Services Region in your Amazon Web Services account.  When it receives the request, Amazon SageMaker creates the endpoint, launches the resources (ML compute instances), and deploys the model(s) on them.   When you call CreateEndpoint, a load call is made to DynamoDB to verify that your endpoint configuration exists. When you read data from a DynamoDB table supporting  Eventually Consistent Reads , the response might not reflect the results of a recently completed write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB, this causes a validation error. If you repeat your read request after a short time, the response should return the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB eventually consistent read.  When Amazon SageMaker receives the request, it sets the endpoint status to Creating. After it creates the endpoint, it sets the status to InService. Amazon SageMaker can then process incoming requests for inferences. To check the status of an endpoint, use the DescribeEndpoint API. If any of the models hosted at this endpoint get model data from an Amazon S3 location, Amazon SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provided. Amazon Web Services STS is activated in your IAM user account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide.   To add the IAM role policies for using this API operation, go to the IAM console, and choose Roles in the left navigation pane. Search the IAM role that you want to grant access to use the CreateEndpoint and CreateEndpointConfig API operations, add the following policies to the role.    Option 1: For a full SageMaker access, search and attach the AmazonSageMakerFullAccess policy.   Option 2: For granting a limited access to an IAM role, paste the following Action elements manually into the JSON file of the IAM role:   "Action": ["sagemaker:CreateEndpoint", "sagemaker:CreateEndpointConfig"]   "Resource": [   "arn:aws:sagemaker:region:account-id:endpoint/endpointName"   "arn:aws:sagemaker:region:account-id:endpoint-config/endpointConfigName"   ]  For more information, see SageMaker API Permissions: Actions, Permissions, and Resources Reference.   
   */
  createEndpoint(params: SageMaker.Types.CreateEndpointInput, callback?: (err: AWSError, data: SageMaker.Types.CreateEndpointOutput) => void): Request<SageMaker.Types.CreateEndpointOutput, AWSError>;
  /**
   * Creates an endpoint using the endpoint configuration specified in the request. Amazon SageMaker uses the endpoint to provision resources and deploy models. You create the endpoint configuration with the CreateEndpointConfig API.   Use this API to deploy models using Amazon SageMaker hosting services.  For an example that calls this method when deploying a model to Amazon SageMaker hosting services, see the Create Endpoint example notebook.    You must not delete an EndpointConfig that is in use by an endpoint that is live or while the UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. To update an endpoint, you must create a new EndpointConfig.  The endpoint name must be unique within an Amazon Web Services Region in your Amazon Web Services account.  When it receives the request, Amazon SageMaker creates the endpoint, launches the resources (ML compute instances), and deploys the model(s) on them.   When you call CreateEndpoint, a load call is made to DynamoDB to verify that your endpoint configuration exists. When you read data from a DynamoDB table supporting  Eventually Consistent Reads , the response might not reflect the results of a recently completed write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB, this causes a validation error. If you repeat your read request after a short time, the response should return the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB eventually consistent read.  When Amazon SageMaker receives the request, it sets the endpoint status to Creating. After it creates the endpoint, it sets the status to InService. Amazon SageMaker can then process incoming requests for inferences. To check the status of an endpoint, use the DescribeEndpoint API. If any of the models hosted at this endpoint get model data from an Amazon S3 location, Amazon SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provided. Amazon Web Services STS is activated in your IAM user account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide.   To add the IAM role policies for using this API operation, go to the IAM console, and choose Roles in the left navigation pane. Search the IAM role that you want to grant access to use the CreateEndpoint and CreateEndpointConfig API operations, add the following policies to the role.    Option 1: For a full SageMaker access, search and attach the AmazonSageMakerFullAccess policy.   Option 2: For granting a limited access to an IAM role, paste the following Action elements manually into the JSON file of the IAM role:   "Action": ["sagemaker:CreateEndpoint", "sagemaker:CreateEndpointConfig"]   "Resource": [   "arn:aws:sagemaker:region:account-id:endpoint/endpointName"   "arn:aws:sagemaker:region:account-id:endpoint-config/endpointConfigName"   ]  For more information, see SageMaker API Permissions: Actions, Permissions, and Resources Reference.   
   */
  createEndpoint(callback?: (err: AWSError, data: SageMaker.Types.CreateEndpointOutput) => void): Request<SageMaker.Types.CreateEndpointOutput, AWSError>;
  /**
   * Creates an endpoint configuration that Amazon SageMaker hosting services uses to deploy models. In the configuration, you identify one or more models, created using the CreateModel API, to deploy and the resources that you want Amazon SageMaker to provision. Then you call the CreateEndpoint API.   Use this API if you want to use Amazon SageMaker hosting services to deploy models into production.   In the request, you define a ProductionVariant, for each model that you want to deploy. Each ProductionVariant parameter also describes the resources that you want Amazon SageMaker to provision. This includes the number and type of ML compute instances to deploy.  If you are hosting multiple models, you also assign a VariantWeight to specify how much traffic you want to allocate to each model. For example, suppose that you want to host two models, A and B, and you assign traffic weight 2 for model A and 1 for model B. Amazon SageMaker distributes two-thirds of the traffic to Model A, and one-third to model B.   When you call CreateEndpoint, a load call is made to DynamoDB to verify that your endpoint configuration exists. When you read data from a DynamoDB table supporting  Eventually Consistent Reads , the response might not reflect the results of a recently completed write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB, this causes a validation error. If you repeat your read request after a short time, the response should return the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB eventually consistent read. 
   */
  createEndpointConfig(params: SageMaker.Types.CreateEndpointConfigInput, callback?: (err: AWSError, data: SageMaker.Types.CreateEndpointConfigOutput) => void): Request<SageMaker.Types.CreateEndpointConfigOutput, AWSError>;
  /**
   * Creates an endpoint configuration that Amazon SageMaker hosting services uses to deploy models. In the configuration, you identify one or more models, created using the CreateModel API, to deploy and the resources that you want Amazon SageMaker to provision. Then you call the CreateEndpoint API.   Use this API if you want to use Amazon SageMaker hosting services to deploy models into production.   In the request, you define a ProductionVariant, for each model that you want to deploy. Each ProductionVariant parameter also describes the resources that you want Amazon SageMaker to provision. This includes the number and type of ML compute instances to deploy.  If you are hosting multiple models, you also assign a VariantWeight to specify how much traffic you want to allocate to each model. For example, suppose that you want to host two models, A and B, and you assign traffic weight 2 for model A and 1 for model B. Amazon SageMaker distributes two-thirds of the traffic to Model A, and one-third to model B.   When you call CreateEndpoint, a load call is made to DynamoDB to verify that your endpoint configuration exists. When you read data from a DynamoDB table supporting  Eventually Consistent Reads , the response might not reflect the results of a recently completed write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB, this causes a validation error. If you repeat your read request after a short time, the response should return the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB eventually consistent read. 
   */
  createEndpointConfig(callback?: (err: AWSError, data: SageMaker.Types.CreateEndpointConfigOutput) => void): Request<SageMaker.Types.CreateEndpointConfigOutput, AWSError>;
  /**
   * Creates an SageMaker experiment. An experiment is a collection of trials that are observed, compared and evaluated as a group. A trial is a set of steps, called trial components, that produce a machine learning model. The goal of an experiment is to determine the components that produce the best model. Multiple trials are performed, each one isolating and measuring the impact of a change to one or more inputs, while keeping the remaining inputs constant. When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK. You can add tags to experiments, trials, trial components and then use the Search API to search for the tags. To add a description to an experiment, specify the optional Description parameter. To add a description later, or to change the description, call the UpdateExperiment API. To get a list of all your experiments, call the ListExperiments API. To view an experiment's properties, call the DescribeExperiment API. To get a list of all the trials associated with an experiment, call the ListTrials API. To create a trial call the CreateTrial API.
   */
  createExperiment(params: SageMaker.Types.CreateExperimentRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateExperimentResponse) => void): Request<SageMaker.Types.CreateExperimentResponse, AWSError>;
  /**
   * Creates an SageMaker experiment. An experiment is a collection of trials that are observed, compared and evaluated as a group. A trial is a set of steps, called trial components, that produce a machine learning model. The goal of an experiment is to determine the components that produce the best model. Multiple trials are performed, each one isolating and measuring the impact of a change to one or more inputs, while keeping the remaining inputs constant. When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK. You can add tags to experiments, trials, trial components and then use the Search API to search for the tags. To add a description to an experiment, specify the optional Description parameter. To add a description later, or to change the description, call the UpdateExperiment API. To get a list of all your experiments, call the ListExperiments API. To view an experiment's properties, call the DescribeExperiment API. To get a list of all the trials associated with an experiment, call the ListTrials API. To create a trial call the CreateTrial API.
   */
  createExperiment(callback?: (err: AWSError, data: SageMaker.Types.CreateExperimentResponse) => void): Request<SageMaker.Types.CreateExperimentResponse, AWSError>;
  /**
   * Create a new FeatureGroup. A FeatureGroup is a group of Features defined in the FeatureStore to describe a Record.  The FeatureGroup defines the schema and features contained in the FeatureGroup. A FeatureGroup definition is composed of a list of Features, a RecordIdentifierFeatureName, an EventTimeFeatureName and configurations for its OnlineStore and OfflineStore. Check Amazon Web Services service quotas to see the FeatureGroups quota for your Amazon Web Services account.  You must include at least one of OnlineStoreConfig and OfflineStoreConfig to create a FeatureGroup. 
   */
  createFeatureGroup(params: SageMaker.Types.CreateFeatureGroupRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateFeatureGroupResponse) => void): Request<SageMaker.Types.CreateFeatureGroupResponse, AWSError>;
  /**
   * Create a new FeatureGroup. A FeatureGroup is a group of Features defined in the FeatureStore to describe a Record.  The FeatureGroup defines the schema and features contained in the FeatureGroup. A FeatureGroup definition is composed of a list of Features, a RecordIdentifierFeatureName, an EventTimeFeatureName and configurations for its OnlineStore and OfflineStore. Check Amazon Web Services service quotas to see the FeatureGroups quota for your Amazon Web Services account.  You must include at least one of OnlineStoreConfig and OfflineStoreConfig to create a FeatureGroup. 
   */
  createFeatureGroup(callback?: (err: AWSError, data: SageMaker.Types.CreateFeatureGroupResponse) => void): Request<SageMaker.Types.CreateFeatureGroupResponse, AWSError>;
  /**
   * Creates a flow definition.
   */
  createFlowDefinition(params: SageMaker.Types.CreateFlowDefinitionRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateFlowDefinitionResponse) => void): Request<SageMaker.Types.CreateFlowDefinitionResponse, AWSError>;
  /**
   * Creates a flow definition.
   */
  createFlowDefinition(callback?: (err: AWSError, data: SageMaker.Types.CreateFlowDefinitionResponse) => void): Request<SageMaker.Types.CreateFlowDefinitionResponse, AWSError>;
  /**
   * Defines the settings you will use for the human review workflow user interface. Reviewers will see a three-panel interface with an instruction area, the item to review, and an input area.
   */
  createHumanTaskUi(params: SageMaker.Types.CreateHumanTaskUiRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateHumanTaskUiResponse) => void): Request<SageMaker.Types.CreateHumanTaskUiResponse, AWSError>;
  /**
   * Defines the settings you will use for the human review workflow user interface. Reviewers will see a three-panel interface with an instruction area, the item to review, and an input area.
   */
  createHumanTaskUi(callback?: (err: AWSError, data: SageMaker.Types.CreateHumanTaskUiResponse) => void): Request<SageMaker.Types.CreateHumanTaskUiResponse, AWSError>;
  /**
   * Starts a hyperparameter tuning job. A hyperparameter tuning job finds the best version of a model by running many training jobs on your dataset using the algorithm you choose and values for hyperparameters within ranges that you specify. It then chooses the hyperparameter values that result in a model that performs the best, as measured by an objective metric that you choose.
   */
  createHyperParameterTuningJob(params: SageMaker.Types.CreateHyperParameterTuningJobRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateHyperParameterTuningJobResponse) => void): Request<SageMaker.Types.CreateHyperParameterTuningJobResponse, AWSError>;
  /**
   * Starts a hyperparameter tuning job. A hyperparameter tuning job finds the best version of a model by running many training jobs on your dataset using the algorithm you choose and values for hyperparameters within ranges that you specify. It then chooses the hyperparameter values that result in a model that performs the best, as measured by an objective metric that you choose.
   */
  createHyperParameterTuningJob(callback?: (err: AWSError, data: SageMaker.Types.CreateHyperParameterTuningJobResponse) => void): Request<SageMaker.Types.CreateHyperParameterTuningJobResponse, AWSError>;
  /**
   * Creates a custom SageMaker image. A SageMaker image is a set of image versions. Each image version represents a container image stored in Amazon Container Registry (ECR). For more information, see Bring your own SageMaker image.
   */
  createImage(params: SageMaker.Types.CreateImageRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateImageResponse) => void): Request<SageMaker.Types.CreateImageResponse, AWSError>;
  /**
   * Creates a custom SageMaker image. A SageMaker image is a set of image versions. Each image version represents a container image stored in Amazon Container Registry (ECR). For more information, see Bring your own SageMaker image.
   */
  createImage(callback?: (err: AWSError, data: SageMaker.Types.CreateImageResponse) => void): Request<SageMaker.Types.CreateImageResponse, AWSError>;
  /**
   * Creates a version of the SageMaker image specified by ImageName. The version represents the Amazon Container Registry (ECR) container image specified by BaseImage.
   */
  createImageVersion(params: SageMaker.Types.CreateImageVersionRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateImageVersionResponse) => void): Request<SageMaker.Types.CreateImageVersionResponse, AWSError>;
  /**
   * Creates a version of the SageMaker image specified by ImageName. The version represents the Amazon Container Registry (ECR) container image specified by BaseImage.
   */
  createImageVersion(callback?: (err: AWSError, data: SageMaker.Types.CreateImageVersionResponse) => void): Request<SageMaker.Types.CreateImageVersionResponse, AWSError>;
  /**
   * Creates a job that uses workers to label the data objects in your input dataset. You can use the labeled data to train machine learning models.  You can select your workforce from one of three providers:   A private workforce that you create. It can include employees, contractors, and outside experts. Use a private workforce when want the data to stay within your organization or when a specific set of skills is required.   One or more vendors that you select from the Amazon Web Services Marketplace. Vendors provide expertise in specific areas.    The Amazon Mechanical Turk workforce. This is the largest workforce, but it should only be used for public data or data that has been stripped of any personally identifiable information.   You can also use automated data labeling to reduce the number of data objects that need to be labeled by a human. Automated data labeling uses active learning to determine if a data object can be labeled by machine or if it needs to be sent to a human worker. For more information, see Using Automated Data Labeling. The data objects to be labeled are contained in an Amazon S3 bucket. You create a manifest file that describes the location of each object. For more information, see Using Input and Output Data. The output can be used as the manifest file for another labeling job or as training data for your machine learning models. You can use this operation to create a static labeling job or a streaming labeling job. A static labeling job stops if all data objects in the input manifest file identified in ManifestS3Uri have been labeled. A streaming labeling job runs perpetually until it is manually stopped, or remains idle for 10 days. You can send new data objects to an active (InProgress) streaming labeling job in real time. To learn how to create a static labeling job, see Create a Labeling Job (API)  in the Amazon SageMaker Developer Guide. To learn how to create a streaming labeling job, see Create a Streaming Labeling Job.
   */
  createLabelingJob(params: SageMaker.Types.CreateLabelingJobRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateLabelingJobResponse) => void): Request<SageMaker.Types.CreateLabelingJobResponse, AWSError>;
  /**
   * Creates a job that uses workers to label the data objects in your input dataset. You can use the labeled data to train machine learning models.  You can select your workforce from one of three providers:   A private workforce that you create. It can include employees, contractors, and outside experts. Use a private workforce when want the data to stay within your organization or when a specific set of skills is required.   One or more vendors that you select from the Amazon Web Services Marketplace. Vendors provide expertise in specific areas.    The Amazon Mechanical Turk workforce. This is the largest workforce, but it should only be used for public data or data that has been stripped of any personally identifiable information.   You can also use automated data labeling to reduce the number of data objects that need to be labeled by a human. Automated data labeling uses active learning to determine if a data object can be labeled by machine or if it needs to be sent to a human worker. For more information, see Using Automated Data Labeling. The data objects to be labeled are contained in an Amazon S3 bucket. You create a manifest file that describes the location of each object. For more information, see Using Input and Output Data. The output can be used as the manifest file for another labeling job or as training data for your machine learning models. You can use this operation to create a static labeling job or a streaming labeling job. A static labeling job stops if all data objects in the input manifest file identified in ManifestS3Uri have been labeled. A streaming labeling job runs perpetually until it is manually stopped, or remains idle for 10 days. You can send new data objects to an active (InProgress) streaming labeling job in real time. To learn how to create a static labeling job, see Create a Labeling Job (API)  in the Amazon SageMaker Developer Guide. To learn how to create a streaming labeling job, see Create a Streaming Labeling Job.
   */
  createLabelingJob(callback?: (err: AWSError, data: SageMaker.Types.CreateLabelingJobResponse) => void): Request<SageMaker.Types.CreateLabelingJobResponse, AWSError>;
  /**
   * Creates a model in Amazon SageMaker. In the request, you name the model and describe a primary container. For the primary container, you specify the Docker image that contains inference code, artifacts (from prior training), and a custom environment map that the inference code uses when you deploy the model for predictions. Use this API to create a model if you want to use Amazon SageMaker hosting services or run a batch transform job. To host your model, you create an endpoint configuration with the CreateEndpointConfig API, and then create an endpoint with the CreateEndpoint API. Amazon SageMaker then deploys all of the containers that you defined for the model in the hosting environment.  For an example that calls this method when deploying a model to Amazon SageMaker hosting services, see Deploy the Model to Amazon SageMaker Hosting Services (Amazon Web Services SDK for Python (Boto 3)).  To run a batch transform using your model, you start a job with the CreateTransformJob API. Amazon SageMaker uses your model and your dataset to get inferences which are then saved to a specified S3 location. In the CreateModel request, you must define a container with the PrimaryContainer parameter. In the request, you also provide an IAM role that Amazon SageMaker can assume to access model artifacts and docker image for deployment on ML compute hosting instances or for batch transform jobs. In addition, you also use the IAM role to manage permissions the inference code needs. For example, if the inference code access any other Amazon Web Services resources, you grant necessary permissions via this role.
   */
  createModel(params: SageMaker.Types.CreateModelInput, callback?: (err: AWSError, data: SageMaker.Types.CreateModelOutput) => void): Request<SageMaker.Types.CreateModelOutput, AWSError>;
  /**
   * Creates a model in Amazon SageMaker. In the request, you name the model and describe a primary container. For the primary container, you specify the Docker image that contains inference code, artifacts (from prior training), and a custom environment map that the inference code uses when you deploy the model for predictions. Use this API to create a model if you want to use Amazon SageMaker hosting services or run a batch transform job. To host your model, you create an endpoint configuration with the CreateEndpointConfig API, and then create an endpoint with the CreateEndpoint API. Amazon SageMaker then deploys all of the containers that you defined for the model in the hosting environment.  For an example that calls this method when deploying a model to Amazon SageMaker hosting services, see Deploy the Model to Amazon SageMaker Hosting Services (Amazon Web Services SDK for Python (Boto 3)).  To run a batch transform using your model, you start a job with the CreateTransformJob API. Amazon SageMaker uses your model and your dataset to get inferences which are then saved to a specified S3 location. In the CreateModel request, you must define a container with the Pri