Package-level declarations
Types
The Amazon Elastic File System (EFS) storage configuration for a SageMaker image.
The configuration for the file system and kernels in a SageMaker image running as a KernelGateway app.
The batch transform input for a monitoring job.
Configuration for the cluster used to run model monitoring jobs.
The baseline constraints resource for a monitoring job.
The CSV format
Container image configuration object for the monitoring job.
Baseline configuration used to validate that the data conforms to the specified constraints and statistics.
The inputs for a monitoring job.
The dataset format of the data to monitor
The endpoint for a monitoring job.
The Json format
The output object for a monitoring job.
The output configuration for monitoring jobs.
Identifies the resources to deploy for a monitoring job.
Networking options for a job, such as network traffic encryption between containers, whether to allow inbound and outbound network calls to and from containers, and the VPC subnets and security groups to use for VPC-enabled jobs.
Information about where and how to store the results of a monitoring job.
The baseline statistics resource for a monitoring job.
Specifies a time limit for how long the monitoring job is allowed to run.
A key-value pair to associate with a resource.
Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC.
Key-value pair to associate as a tag for the resource
A custom SageMaker image.
A collection of settings that apply to spaces of Amazon SageMaker Studio. These settings are specified when the Create/Update Domain API is called.
The JupyterServer app settings.
The kernel gateway app settings.
A collection of settings that apply to an RSessionGateway app.
A collection of settings that configure user interaction with the RStudioServerPro app.
A collection of settings that update the current configuration for the RStudioServerPro Domain-level app.
A collection of Domain settings.
Specifies options when sharing an Amazon SageMaker Studio notebook. These settings are specified as part of DefaultUserSettings when the CreateDomain API is called, and as part of UserSettings when the CreateUserProfile API is called.
A collection of settings that apply to users of Amazon SageMaker Studio. These settings are specified when the CreateUserProfile API is called, and as DefaultUserSettings when the CreateDomain API is called.
A key-value pair to associate with a resource.
Configuration specifying how to treat different headers. If no headers are specified SageMaker will by default base64 encode when capturing the data.
The Amazon S3 location and configuration for storing inference request and response data.
The metadata of the endpoint on which the inference experiment ran.
The configuration for the infrastructure that the model will be deployed to.
Contains information about the deployment options of a model.
The infrastructure configuration for deploying the model to a real-time inference endpoint.
The duration for which you want the inference experiment to run.
The configuration of ShadowMode inference experiment type. Use this field to specify a production variant which takes all the inference requests, and a shadow variant to which Amazon SageMaker replicates a percentage of the inference requests. For the shadow variant also specify the percentage of requests that Amazon SageMaker replicates.
The name and sampling percentage of a shadow variant.
A key-value pair to associate with a resource.
The batch transform input for a monitoring job.
Configuration for the cluster used to run model monitoring jobs.
The baseline constraints resource for a monitoring job.
The CSV format
The dataset format of the data to monitor
The endpoint for a monitoring job.
The Json format
Container image configuration object for the monitoring job.
Baseline configuration used to validate that the data conforms to the specified constraints and statistics.
The inputs for a monitoring job.
Ground truth input provided in S3
The output object for a monitoring job.
The output configuration for monitoring jobs.
Identifies the resources to deploy for a monitoring job.
Networking options for a job, such as network traffic encryption between containers, whether to allow inbound and outbound network calls to and from containers, and the VPC subnets and security groups to use for VPC-enabled jobs.
Information about where and how to store the results of a monitoring job.
Specifies a time limit for how long the monitoring job is allowed to run.
A key-value pair to associate with a resource.
Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC.
Business details.
The content of the model card.
item of evaluation details
Intended usage of model.
Linear graph metric.
item in metric groups
Overview about the model.
Overview about the inference.
Metadata information related to model package version
the objective function the model will optimize for.
objective function that training job is optimized for.
An optional Key Management Service key to encrypt, decrypt, and re-encrypt model card content for regulated workloads with highly sensitive data.
metric data
A key-value pair to associate with a resource.
Overview about the training.
training hyper parameter
training metric data.
Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.
The batch transform input for a monitoring job.
Configuration for the cluster used to run model monitoring jobs.
The baseline constraints resource for a monitoring job.
The CSV format
The dataset format of the data to monitor
The endpoint for a monitoring job.
The Json format
Container image configuration object for the monitoring job.
Baseline configuration used to validate that the data conforms to the specified constraints and statistics.
The inputs for a monitoring job.
The output object for a monitoring job.
The output configuration for monitoring jobs.
Identifies the resources to deploy for a monitoring job.
Networking options for a job, such as network traffic encryption between containers, whether to allow inbound and outbound network calls to and from containers, and the VPC subnets and security groups to use for VPC-enabled jobs.
Information about where and how to store the results of a monitoring job.
Specifies a time limit for how long the monitoring job is allowed to run.
A key-value pair to associate with a resource.
Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC.
Additional Inference Specification specifies details about inference jobs that can be run with models based on this model package.AdditionalInferenceSpecifications can be added to existing model packages using AdditionalInferenceSpecificationsToAdd.
Contains bias metrics for a model.
Describes the Docker container for the model package.
The metadata properties associated with the model package versions.
Describes the input source of a transform job and the way the transform job consumes it.
Represents the drift check baselines that can be used when the model monitor is set using the model package.
Represents the drift check bias baselines that can be used when the model monitor is set using the model package.
Contains explainability metrics for a model.
Represents the drift check data quality baselines that can be used when the model monitor is set using the model package.
Represents the drift check model quality baselines that can be used when the model monitor is set using the model package.
Sets the environment variables in the Docker container
Contains explainability metrics for a model.
Represents a File Source Object.
A key-value pair to associate with a resource.
Details about inference jobs that can be run with models based on this model package.
Metadata properties of the tracking entity, trial, or trial component.
Represents a Metric Source Object.
Metrics that measure the quality of the input data for a model.
A structure that contains model metrics reports.
Metrics that measure the quality of a model.
Describes the S3 data source.
Specifies an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your Amazon SageMaker account or an algorithm in AWS Marketplace that you are subscribed to.
Details about the algorithm that was used to create the model package.
Details about the current status of the model package.
Represents the overall status of a model package.
A key-value pair to associate with a resource.
Describes the input source of a transform job and the way the transform job consumes it.
Defines the input needed to run a transform job using the inference specification specified in the algorithm.
Describes the results of a transform job.
Describes the resources, including ML instance types and ML instance count, to use for transform job.
Contains data, such as the inputs and targeted instance types that are used in the process of validating the model package.
Specifies configurations for one or more transform jobs that Amazon SageMaker runs to test the model package.
The batch transform input for a monitoring job.
Configuration for the cluster used to run model monitoring jobs.
The baseline constraints resource for a monitoring job.
The CSV format
The dataset format of the data to monitor
The endpoint for a monitoring job.
The Json format
Container image configuration object for the monitoring job.
Baseline configuration used to validate that the data conforms to the specified constraints and statistics.
The inputs for a monitoring job.
Ground truth input provided in S3
The output object for a monitoring job.
The output configuration for monitoring jobs.
Identifies the resources to deploy for a monitoring job.
Networking options for a job, such as network traffic encryption between containers, whether to allow inbound and outbound network calls to and from containers, and the VPC subnets and security groups to use for VPC-enabled jobs.
Information about where and how to store the results of a monitoring job.
Specifies a time limit for how long the monitoring job is allowed to run.
A key-value pair to associate with a resource.
Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC.
Baseline configuration used to validate that the data conforms to the specified constraints and statistics.
The batch transform input for a monitoring job.
Configuration for the cluster used to run model monitoring jobs.
The configuration object that specifies the monitoring schedule and defines the monitoring job.
The baseline constraints resource for a monitoring job.
The CSV format
The dataset format of the data to monitor
The endpoint for a monitoring job.
The Json format
Container image configuration object for the monitoring job.
Summary of information about monitoring job
The inputs for a monitoring job.
Defines the monitoring job.
The output object for a monitoring job.
The output configuration for monitoring jobs.
Identifies the resources to deploy for a monitoring job.
Networking options for a job, such as network traffic encryption between containers, whether to allow inbound and outbound network calls to and from containers, and the VPC subnets and security groups to use for VPC-enabled jobs.
Information about where and how to store the results of a monitoring job.
Configuration details about the monitoring schedule.
The baseline statistics resource for a monitoring job.
Specifies a time limit for how long the monitoring job is allowed to run.
A key-value pair to associate with a resource.
Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC.
Information about a parameter used to provision a product.
A key-value pair to associate with a resource.
Provisioned ServiceCatalog Details
Input ServiceCatalog Provisioning Details
A custom SageMaker image.
The JupyterServer app settings.
The kernel gateway app settings.
A collection of settings that apply to spaces of Amazon SageMaker Studio. These settings are specified when the CreateSpace API is called.
A custom SageMaker image.
The JupyterServer app settings.
The kernel gateway app settings.
A collection of settings that configure user interaction with the RStudioServerPro app.
Specifies options when sharing an Amazon SageMaker Studio notebook. These settings are specified as part of DefaultUserSettings when the CreateDomain API is called, and as part of UserSettings when the CreateUserProfile API is called.
A collection of settings that apply to users of Amazon SageMaker Studio. These settings are specified when the CreateUserProfile API is called, and as DefaultUserSettings when the CreateDomain API is called.