AddToScheme adds the types in this group-version to the given scheme.

GroupVersion is the API Group Version used to register the objects.

SchemeBuilder is used to add go types to the GroupVersionKind scheme.

A structure describing the source of an action.

Lists the properties of an action.

A structure of additional Inference Specification.

A data source used for training or inference that is in addition to the input dataset or model data.

An Amazon CloudWatch alarm configured to monitor metrics on an endpoint.

Specifies the training algorithm to use in a CreateTrainingJob (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingJob.html) request.

Represents the overall status of an algorithm.

Provides summary information about an algorithm.

Defines a training job and a batch transform job that SageMaker runs to validate your algorithm.

Specifies configurations for one or more training jobs that SageMaker runs to test the algorithm.

App is the Schema for the Apps API +kubebuilder:object:root=true +kubebuilder:subresource:status +kubebuilder:printcolumn:name="STATUS",type=string,priority=0,JSONPath=`.status.status`.

Details about an Amazon SageMaker app.

The configuration for running a SageMaker image as a KernelGateway app.

AppList contains a list of App +kubebuilder:object:root=true.

AppSpec defines the desired state of App.

Configuration to run a processing job in a specified container image.

AppStatus defines the observed state of App.

The ID and ID type of an artifact source.

Lists a summary of the properties of an artifact.

Lists a summary of the properties of an association.

Configures the behavior of the client used by SageMaker to interact with the model container during asynchronous inference.

Specifies configuration for how an endpoint performs asynchronous inference.

Specifies the configuration for notifications of inference results for asynchronous inference.

Specifies the configuration for asynchronous inference invocation outputs.

Configuration for Athena Dataset Definition input.

Information about a candidate produced by an AutoML training job, including its status, steps, and other properties.

Stores the configuration information for how a candidate is generated (optional).

A channel is a named input source that training algorithms can consume.

A list of container definitions that describe the different containers that make up an AutoML candidate.

A channel is a named input source that training algorithms can consume.

Metadata for an AutoML job step.

Provides a summary about an AutoML job.

The output data configuration.

Describes the Amazon S3 data source.

Security options.

The name and an example value of the hyperparameter that you want to use in Autotune.

Automatic rollback configuration for handling endpoint deployment failures and recovery.

A flag to indicate if you want to use Autotune to automatically find optimal values for the following fields: - ParameterRanges (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTuningJobConfig.html#sagemaker-Type-HyperParameterTuningJobConfig-ParameterRanges): The names and ranges of parameters that a hyperparameter tuning job can optimize.

The error code and error description associated with the resource.

Provides summary information about the model package.

Contains bias metrics for a model.

Update policy for a blue/green deployment.

Details on the cache hit of a pipeline execution step.

Metadata about a callback step.

Specifies the type and size of the endpoint capacity to activate for a blue/green deployment, a rolling deployment, or a rollback strategy.

Configuration specifying how to treat different headers.

Specifies data Model Monitor will capture.

A list of categorical hyperparameters to tune.

Defines the possible values for a categorical hyperparameter.

A channel is a named input source that training algorithms can consume.

Defines a named input source, called a channel, to be used by an algorithm.

Contains information about the output location for managed spot training checkpoint data.

The container for the metadata for the ClarifyCheck step.

The configuration for the SHAP baseline (https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-feature-attribute-shap-baselines.html) (also called the background or reference dataset) of the Kernal SHAP algorithm.

Details of an instance group in a SageMaker HyperPod cluster.

The specifications of an instance group that you need to define.

Details of an instance in a SageMaker HyperPod cluster.

The LifeCycle configuration for a SageMaker HyperPod cluster.

Details of an instance (also called a node interchangeably) in a SageMaker HyperPod cluster.

Lists a summary of the properties of an instance (also called a node interchangeably) of a SageMaker HyperPod cluster.

Lists a summary of the properties of a SageMaker HyperPod cluster.

The Code Editor application settings.

Specifies summary information about a Git repository.

Configuration for your collection.

Configuration information for the Amazon SageMaker Debugger output tensor collections.

A summary of a model compilation job.

Describes the container, as part of model definition.

A structure describing the source of a context.

Lists a summary of the properties of a context.

A list of continuous hyperparameters to tune.

Defines the possible values for a continuous hyperparameter.

The settings for assigning a custom file system to a user profile or space for an Amazon SageMaker Domain.

A custom SageMaker image.

A customized metric.

Details about the POSIX identity that is used for file system operations.

Configuration to control how SageMaker captures inference data.

The currently active data capture configuration used by your Endpoint.

The meta data of the Glue table which serves as data catalog for the OfflineStore.

The data structure used to specify the data to be used for inference in a batch transform job and to associate the data that is relevant to the prediction results in the output.

Information about the container that a data quality monitoring job runs.

Configuration for monitoring constraints and monitoring statistics.

DataQualityJobDefinition is the Schema for the DataQualityJobDefinitions API +kubebuilder:object:root=true +kubebuilder:subresource:status.

DataQualityJobDefinitionList contains a list of DataQualityJobDefinition +kubebuilder:object:root=true.

DataQualityJobDefinitionSpec defines the desired state of DataQualityJobDefinition.

DataQualityJobDefinitionStatus defines the observed state of DataQualityJobDefinition.

The input for the data quality monitoring job.

Configuration for Dataset Definition inputs.

Describes the location of the channel data.

Configuration information for the Amazon SageMaker Debugger hook parameters, metric and tensor collections, and storage paths.

Configuration information for SageMaker Debugger rules for debugging.

Information about the status of the rule evaluation.

A collection of default EBS storage settings that applies to private spaces created within a domain or user profile.

The default storage settings for a private space.

Gets the Amazon EC2 Container Registry path of the docker image of the model that is hosted in this ProductionVariant (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ProductionVariant.html).

The deployment configuration for an endpoint, which contains the desired deployment strategy and rollback configurations.

A set of recommended deployment configurations for the model.

Contains information about a stage in an edge deployment plan.

Contains information summarizing the deployment stage results.

Information that SageMaker Neo automatically derived about the model.

Specifies weight and capacity values for a production variant.

Contains information summarizing device details and deployment status.

Summary of the device fleet.

Summary of the device.

The model deployment settings for the SageMaker Canvas application.

A collection of settings that configure the domain's Docker interaction.

Domain is the Schema for the Domains API +kubebuilder:object:root=true +kubebuilder:subresource:status +kubebuilder:printcolumn:name="DOMAIN-ID",type=string,priority=0,JSONPath=`.status.domainID` +kubebuilder:printcolumn:name="STATUS",type=string,priority=0,JSONPath=`.status.status`.

The domain's details.

DomainList contains a list of Domain +kubebuilder:object:root=true.

A collection of settings that apply to the SageMaker Domain.

A collection of Domain configuration settings to update.

DomainSpec defines the desired state of Domain.

DomainStatus defines the observed state of Domain.

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.

Represents the drift check explainability baselines that can be used when the model monitor is set using the model package.

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.

An object with the recommended values for you to specify when creating an autoscaling policy.

A collection of EBS storage settings that applies to private spaces.

Contains information about the configuration of a model in a deployment.

Contains information summarizing an edge deployment plan.

Contains information summarizing the deployment stage results.

The model on the edge device.

Status of edge devices with this model.

Summary of model on edge device.

The output configuration.

Summary of edge packaging job.

The output of a SageMaker Edge Manager deployable resource.

A file system, created by you in Amazon EFS, that you assign to a user profile or space for an Amazon SageMaker Domain.

The settings for assigning a custom Amazon EFS file system to a user profile or space for an Amazon SageMaker Domain.

The configurations and outcomes of an Amazon EMR step execution.

Endpoint is the Schema for the Endpoints API +kubebuilder:object:root=true +kubebuilder:subresource:status +kubebuilder:printcolumn:name="FAILURE-REASON",type=string,priority=1,JSONPath=`.status.failureReason` +kubebuilder:printcolumn:name="STATUS",type=string,priority=0,JSONPath=`.status.endpointStatus`.

A hosted endpoint for real-time inference.

EndpointConfig is the Schema for the EndpointConfigs API +kubebuilder:object:root=true +kubebuilder:subresource:status.

EndpointConfigList contains a list of EndpointConfig +kubebuilder:object:root=true.

EndpointConfigSpec defines the desired state of EndpointConfig.

EndpointConfigStatus defines the observed state of EndpointConfig.

Provides summary information for an endpoint configuration.

Details about a customer endpoint that was compared in an Inference Recommender job.

Input object for the endpoint.

The endpoint configuration for the load test.

EndpointList contains a list of Endpoint +kubebuilder:object:root=true.

The metadata of the endpoint.

The endpoint configuration made by Inference Recommender during a recommendation job.

EndpointSpec defines the desired state of Endpoint.

EndpointStatus defines the observed state of Endpoint.

Provides summary information for an endpoint.

A list of environment parameters suggested by the Amazon SageMaker Inference Recommender.

The properties of an experiment as returned by the Search (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_Search.html) API.

Associates a SageMaker job as a trial component with an experiment and trial.

A summary of the properties of an experiment.

Contains explainability metrics for a model.

The container for the metadata for Fail step.

A list of features.

FeatureGroup is the Schema for the FeatureGroups API +kubebuilder:object:root=true +kubebuilder:subresource:status +kubebuilder:printcolumn:name="FAILURE-REASON",type=string,priority=1,JSONPath=`.status.failureReason` +kubebuilder:printcolumn:name="STATUS",type=string,priority=0,JSONPath=`.status.featureGroupStatus`.

Amazon SageMaker Feature Store stores features in a collection called Feature Group.

FeatureGroupList contains a list of FeatureGroup +kubebuilder:object:root=true.

FeatureGroupSpec defines the desired state of FeatureGroup.

FeatureGroupStatus defines the observed state of FeatureGroup.

The name, ARN, CreationTime, FeatureGroup values, LastUpdatedTime and EnableOnlineStorage status of a FeatureGroup.

The metadata for a feature.

Contains details regarding the file source.

Specifies a file system data source for a channel.

The best candidate result from an AutoML training job.

Shows the latest objective metric emitted by a training job that was launched by a hyperparameter tuning job.

Contains information about where human output will be stored.

Contains summary information about the flow definition.

The generative AI settings for the SageMaker Canvas application.

Information about hub content.

Information about a hub.

Container for human task user interface information.

Specifies which training algorithm to use for training jobs that a hyperparameter tuning job launches and the metrics to monitor.

Defines a hyperparameter to be used by an algorithm.

Defines the training jobs launched by a hyperparameter tuning job.

The container for the summary information about a training job.

The configuration for hyperparameter tuning resources for use in training jobs launched by the tuning job.

HyperParameterTuningJob is the Schema for the HyperParameterTuningJobs API +kubebuilder:object:root=true +kubebuilder:subresource:status +kubebuilder:printcolumn:name="FAILURE-REASON",type=string,priority=1,JSONPath=`.status.failureReason` +kubebuilder:printcolumn:name="STATUS",type=string,priority=0,JSONPath=`.status.hyperParameterTuningJobStatus`.

A structure that contains runtime information about both current and completed hyperparameter tuning jobs.

Configures a hyperparameter tuning job.

The total resources consumed by your hyperparameter tuning job.

HyperParameterTuningJobList contains a list of HyperParameterTuningJob +kubebuilder:object:root=true.

Defines the objective metric for a hyperparameter tuning job.

An entity returned by the SearchRecord (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_SearchRecord.html) API containing the properties of a hyperparameter tuning job.

HyperParameterTuningJobSpec defines the desired state of HyperParameterTuningJob.

HyperParameterTuningJobStatus defines the observed state of HyperParameterTuningJob.

Provides summary information about a hyperparameter tuning job.

Specifies the configuration for a hyperparameter tuning job that uses one or more previous hyperparameter tuning jobs as a starting point.

The IAM Identity details associated with the user.

The Amazon SageMaker Canvas application setting where you configure OAuth for connecting to an external data source, such as Snowflake.

A SageMaker image.

Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC).

A version of a SageMaker Image.

InferenceComponent is the Schema for the InferenceComponents API +kubebuilder:object:root=true +kubebuilder:subresource:status +kubebuilder:printcolumn:name="FAILURE-REASON",type=string,priority=1,JSONPath=`.status.failureReason` +kubebuilder:printcolumn:name="STATUS",type=string,priority=0,JSONPath=`.status.inferenceComponentStatus`.

Defines the compute resources to allocate to run a model that you assign to an inference component.

Defines a container that provides the runtime environment for a model that you deploy with an inference component.

Details about the resources that are deployed with this inference component.

InferenceComponentList contains a list of InferenceComponent +kubebuilder:object:root=true.

Runtime settings for a model that is deployed with an inference component.

Details about the runtime settings for the model that is deployed with the inference component.

InferenceComponentSpec defines the desired state of InferenceComponent.

Details about the resources to deploy with this inference component, including the model, container, and compute resources.

Details about the resources that are deployed with this inference component.

Settings that take effect while the model container starts up.

InferenceComponentStatus defines the observed state of InferenceComponent.

A summary of the properties of an inference component.

Specifies details about how containers in a multi-container endpoint are run.

The Amazon S3 location and configuration for storing inference request and response data.

The start and end times of an inference experiment.

Lists a summary of properties of an inference experiment.

The metrics for an existing endpoint compared in an Inference Recommender job.

A list of recommendations made by Amazon SageMaker Inference Recommender.

A structure that contains a list of recommendation jobs.

Defines how to perform inference generation after a training job is run.

Configuration information for the infrastructure health check of a training job.

Contains information about the location of input model artifacts, the name and shape of the expected data inputs, and the framework in which the model was trained.

Defines an instance group for heterogeneous cluster training.

For a hyperparameter of the integer type, specifies the range that a hyperparameter tuning job searches.

Defines the possible values for an integer hyperparameter.

The settings for the JupyterLab application.

The JupyterServer app settings.

The Amazon SageMaker Canvas application setting where you configure document querying.

The KernelGateway app settings.

Provides configuration information for auto-labeling of your data objects.

Provides summary information for a work team.

Specifies the location of the output produced by the labeling job.

Output configuration information for a labeling job.

Configure encryption on the storage volume attached to the ML compute instance used to run automated data labeling model training and inference.

The Amazon S3 location of the input data objects.

An Amazon SNS data source used for streaming labeling jobs.

Provides summary information about a labeling job.

Metadata for a Lambda step.

A value that indicates whether the update was successful.

Lists a summary of the properties of a lineage group.

Metadata properties of the tracking entity, trial, or trial component.

The name, value, and date and time of a metric that was emitted to Amazon CloudWatch.

Information about the metric for a candidate produced by an AutoML job.

Specifies a metric that the training algorithm writes to stderr or stdout.

Details about the metrics source.

Model is the Schema for the Models API +kubebuilder:object:root=true +kubebuilder:subresource:status.

The properties of a model as returned by the Search (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_Search.html) API.

The access configuration file to control access to the ML model.

Provides information about the location that is configured for storing model artifacts.

Docker container image configuration object for the model bias job.

The configuration for a baseline model bias job.

ModelBiasJobDefinition is the Schema for the ModelBiasJobDefinitions API +kubebuilder:object:root=true +kubebuilder:subresource:status.

ModelBiasJobDefinitionList contains a list of ModelBiasJobDefinition +kubebuilder:object:root=true.

ModelBiasJobDefinitionSpec defines the desired state of ModelBiasJobDefinition.

ModelBiasJobDefinitionStatus defines the observed state of ModelBiasJobDefinition.

Inputs for the model bias job.

An Amazon SageMaker Model Card.

The artifacts of the model card export job.

The summary of the Amazon SageMaker Model Card export job.

Configure the export output details for an Amazon SageMaker Model Card.

Configure the security settings to protect model card data.

A summary of the model card.

A summary of a specific version of the model card.

Configures the timeout and maximum number of retries for processing a transform job invocation.

Defines the model configuration.

An endpoint that hosts a model displayed in the Amazon SageMaker Model Dashboard.

An alert action taken to light up an icon on the Amazon SageMaker Model Dashboard when an alert goes into InAlert status.

The model card for a model displayed in the Amazon SageMaker Model Dashboard.

A monitoring schedule for a model displayed in the Amazon SageMaker Model Dashboard.

Data quality constraints and statistics for a model.

Specifies the location of ML model data to deploy.

Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.

Provides information about the endpoint of the model deployment.

Docker container image configuration object for the model explainability job.

The configuration for a baseline model explainability job.

ModelExplainabilityJobDefinition is the Schema for the ModelExplainabilityJobDefinitions API +kubebuilder:object:root=true +kubebuilder:subresource:status.

ModelExplainabilityJobDefinitionList contains a list of ModelExplainabilityJobDefinition +kubebuilder:object:root=true.

ModelExplainabilityJobDefinitionSpec defines the desired state of ModelExplainabilityJobDefinition.

ModelExplainabilityJobDefinitionStatus defines the observed state of ModelExplainabilityJobDefinition.

Inputs for the model explainability job.

Input object for the model.

The model latency threshold.

ModelList contains a list of Model +kubebuilder:object:root=true.

Part of the search expression.

A summary of the model metadata.

Contains metrics captured from a model.

ModelPackage is the Schema for the ModelPackages API +kubebuilder:object:root=true +kubebuilder:subresource:status +kubebuilder:printcolumn:name="STATUS",type=string,priority=0,JSONPath=`.status.modelPackageStatus`.

A versioned model that can be deployed for SageMaker inference.

Describes the Docker container for the model package.

ModelPackageGroup is the Schema for the ModelPackageGroups API +kubebuilder:object:root=true +kubebuilder:subresource:status +kubebuilder:printcolumn:name="STATUS",type=string,priority=0,JSONPath=`.status.modelPackageGroupStatus`.

A group of versioned models in the model registry.

ModelPackageGroupList contains a list of ModelPackageGroup +kubebuilder:object:root=true.

ModelPackageGroupSpec defines the desired state of ModelPackageGroup.

ModelPackageGroupStatus defines the observed state of ModelPackageGroup.

Summary information about a model group.

ModelPackageList contains a list of ModelPackage +kubebuilder:object:root=true.

ModelPackageSpec defines the desired state of ModelPackage.

ModelPackageStatus defines the observed state of ModelPackage.

Specifies the validation and image scan statuses of the model package.

Represents the overall status of a model package.

Provides summary information about a model package.

Contains data, such as the inputs and targeted instance types that are used in the process of validating the model package.

Specifies batch transform jobs that SageMaker runs to validate your model package.

Model quality statistics and constraints.

Container image configuration object for the monitoring job.

Configuration for monitoring constraints and monitoring statistics.

ModelQualityJobDefinition is the Schema for the ModelQualityJobDefinitions API +kubebuilder:object:root=true +kubebuilder:subresource:status.

ModelQualityJobDefinitionList contains a list of ModelQualityJobDefinition +kubebuilder:object:root=true.

ModelQualityJobDefinitionSpec defines the desired state of ModelQualityJobDefinition.

ModelQualityJobDefinitionStatus defines the observed state of ModelQualityJobDefinition.

The input for the model quality monitoring job.

The model registry settings for the SageMaker Canvas application.

ModelSpec defines the desired state of Model.

ModelStatus defines the observed state of Model.

Metadata for Model steps.

Provides summary information about a model.

Contains information about the deployment options of a model.

Summary of the deployment configuration of a model.

Provides summary information of an alert's history.

Provides summary information about a monitor alert.

Container image configuration object for the monitoring job.

Configuration for monitoring constraints and monitoring statistics.

Configuration for the cluster used to run model monitoring jobs.

The constraints resource for a monitoring job.

Represents the CSV dataset format used when running a monitoring job.

Summary of information about the last monitoring job to run.

The ground truth labels for the dataset used for the monitoring job.

The inputs for a monitoring job.

Defines the monitoring job.

Summary information about a monitoring job.

Represents the JSON dataset format used when running a monitoring job.

The networking configuration for 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.

Information about where and how you want to store the results of a monitoring job.

MonitoringSchedule is the Schema for the MonitoringSchedules API +kubebuilder:object:root=true +kubebuilder:subresource:status +kubebuilder:printcolumn:name="FAILURE-REASON",type=string,priority=1,JSONPath=`.status.failureReason` +kubebuilder:printcolumn:name="STATUS",type=string,priority=0,JSONPath=`.status.monitoringScheduleStatus`.

A schedule for a model monitoring job.

Configures the monitoring schedule and defines the monitoring job.

MonitoringScheduleList contains a list of MonitoringSchedule +kubebuilder:object:root=true.

MonitoringScheduleSpec defines the desired state of MonitoringSchedule.

MonitoringScheduleStatus defines the observed state of MonitoringSchedule.

Summarizes the monitoring schedule.

The statistics resource for a monitoring job.

A time limit for how long the monitoring job is allowed to run before stopping.

Specifies additional configuration for hosting multi-model endpoints.

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.

NotebookInstance is the Schema for the NotebookInstances API +kubebuilder:object:root=true +kubebuilder:subresource:status +kubebuilder:printcolumn:name="FAILURE-REASON",type=string,priority=1,JSONPath=`.status.failureReason` +kubebuilder:printcolumn:name="STATUS",type=string,priority=0,JSONPath=`.status.notebookInstanceStatus`.

NotebookInstanceLifecycleConfig is the Schema for the NotebookInstanceLifecycleConfigs API +kubebuilder:object:root=true +kubebuilder:subresource:status +kubebuilder:printcolumn:name="CREATION-TIME",type=date,priority=0,JSONPath=`.status.creationTime` +kubebuilder:printcolumn:name="LAST-MODIFIED-TIME",type=date,priority=0,JSONPath=`.status.lastModifiedTime`.

NotebookInstanceLifecycleConfigList contains a list of NotebookInstanceLifecycleConfig +kubebuilder:object:root=true.

NotebookInstanceLifecycleConfigSpec defines the desired state of NotebookInstanceLifecycleConfig.

NotebookInstanceLifecycleConfigStatus defines the observed state of NotebookInstanceLifecycleConfig.

Provides a summary of a notebook instance lifecycle configuration.

Contains the notebook instance lifecycle configuration script.

NotebookInstanceList contains a list of NotebookInstance +kubebuilder:object:root=true.

NotebookInstanceSpec defines the desired state of NotebookInstance.

NotebookInstanceStatus defines the observed state of NotebookInstance.

Provides summary information for an SageMaker notebook instance.

Specifies the number of training jobs that this hyperparameter tuning job launched, categorized by the status of their objective metric.

The configuration of an OfflineStore.

The status of OfflineStore.

Use this to specify the Amazon Web Services Key Management Service (KMS) Key ID, or KMSKeyId, for at rest data encryption.

Updates the feature group online store configuration.

The security configuration for OnlineStore.

Contains information about the output location for the compiled model and the target device that the model runs on.

Provides information about how to store model training results (model artifacts).

An output parameter of a pipeline step.

The collection of ownership settings for a space.

Specifies summary information about the ownership settings.

Configuration that controls the parallelism of the pipeline.

Assigns a value to a named Pipeline parameter.

Specifies ranges of integer, continuous, and categorical hyperparameters that a hyperparameter tuning job searches.

The trial that a trial component is associated with and the experiment the trial is part of.

A previously completed or stopped hyperparameter tuning job to be used as a starting point for a new hyperparameter tuning job.

The summary of an in-progress deployment when an endpoint is creating or updating with a new endpoint configuration.

The production variant summary for a deployment when an endpoint is creating or updating with the CreateEndpoint (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateEndpoint.html) or UpdateEndpoint (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_UpdateEndpoint.html) operations.

Pipeline is the Schema for the Pipelines API +kubebuilder:object:root=true +kubebuilder:subresource:status +kubebuilder:printcolumn:name="STATUS",type=string,priority=0,JSONPath=`.status.pipelineStatus`.

A SageMaker Model Building Pipeline instance.

The location of the pipeline definition stored in Amazon S3.

PipelineExecution is the Schema for the PipelineExecutions API +kubebuilder:object:root=true +kubebuilder:subresource:status +kubebuilder:printcolumn:name="FAILURE-REASON",type=string,priority=1,JSONPath=`.status.failureReason` +kubebuilder:printcolumn:name="STATUS",type=string,priority=0,JSONPath=`.status.pipelineExecutionStatus`.

An execution of a pipeline.

PipelineExecutionList contains a list of PipelineExecution +kubebuilder:object:root=true.

PipelineExecutionSpec defines the desired state of PipelineExecution.

PipelineExecutionStatus defines the observed state of PipelineExecution.

An execution of a step in a pipeline.

A pipeline execution summary.

Specifies the names of the experiment and trial created by a pipeline.

PipelineList contains a list of Pipeline +kubebuilder:object:root=true.

PipelineSpec defines the desired state of Pipeline.

PipelineStatus defines the observed state of Pipeline.

A summary of a pipeline.

A specification for a predefined metric.

Configuration for the cluster used to run a processing job.

Configuration for processing job outputs in Amazon SageMaker Feature Store.

The inputs for a processing job.

ProcessingJob is the Schema for the ProcessingJobs API +kubebuilder:object:root=true +kubebuilder:subresource:status +kubebuilder:printcolumn:name="FAILURE-REASON",type=string,priority=1,JSONPath=`.status.failureReason` +kubebuilder:printcolumn:name="STATUS",type=string,priority=0,JSONPath=`.status.processingJobStatus`.

An Amazon SageMaker processing job that is used to analyze data and evaluate models.

ProcessingJobList contains a list of ProcessingJob +kubebuilder:object:root=true.

ProcessingJobSpec defines the desired state of ProcessingJob.

ProcessingJobStatus defines the observed state of ProcessingJob.

Metadata for a processing job step.

Summary of information about a processing job.

Describes the results of a processing job.

Configuration for uploading output from the processing container.

Identifies the resources, ML compute instances, and ML storage volumes to deploy for a processing job.

Configuration for downloading input data from Amazon S3 into the processing container.

Configuration for uploading output data to Amazon S3 from the processing container.

Configures conditions under which the processing job should be stopped, such as how long the processing job has been running.

Identifies a model that you want to host and the resources chosen to deploy for hosting it.

Specifies configuration for a core dump from the model container when the process crashes.

Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic.

Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.

Specifies the serverless configuration for an endpoint variant.

Specifies the serverless update concurrency configuration for an endpoint variant.

Describes the status of the production variant.

Describes weight and capacities for a production variant associated with an endpoint.

Configuration information for Amazon SageMaker Debugger system monitoring, framework profiling, and storage paths.

Configuration information for updating the Amazon SageMaker Debugger profile parameters, system and framework metrics configurations, and storage paths.

Configuration information for profiling rules.

Information about the status of the rule evaluation.

The properties of a project as returned by the Search API.

Information about a project.

Container for the metadata for a Quality check step.

A set of filters to narrow the set of lineage entities connected to the StartArn(s) returned by the QueryLineage API action.

The infrastructure configuration for deploying the model to a real-time inference endpoint.

The recommended configuration to use for Real-Time Inference.

Provides information about the output configuration for the compiled model.

Specifies mandatory fields for running an Inference Recommender job directly in the CreateInferenceRecommendationsJob (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateInferenceRecommendationsJob.html) API.

The input configuration of the recommendation job.

Provides information about the output configuration for the compiled model.

The configuration for the payload for a recommendation job.

Specifies conditions for stopping a job.

The metrics of recommendations.

Configuration for Redshift Dataset Definition input.

Metadata for a register model job step.

Configuration for remote debugging for the CreateTrainingJob (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingJob.html) API.

Configuration for remote debugging for the UpdateTrainingJob (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_UpdateTrainingJob.html) API.

A description of an error that occurred while rendering the template.

Specifies an authentication configuration for the private docker registry where your model image is hosted.

A resource catalog containing all of the resources of a specific resource type within a resource owner account.

Describes the resources, including machine learning (ML) compute instances and ML storage volumes, to use for model training.

The ResourceConfig to update KeepAlivePeriodInSeconds.

Specifies the maximum number of training jobs and parallel training jobs that a hyperparameter tuning job can launch.

Specifies the ARN's of a SageMaker image and SageMaker image version, and the instance type that the version runs on.

The retention policy for data stored on an Amazon Elastic File System volume.

The retry strategy to use when a training job fails due to an InternalServerError.

Specifies a rolling deployment strategy for updating a SageMaker endpoint.

A collection of settings that configure user interaction with the RStudioServerPro app.

A collection of settings that configure the RStudioServerPro Domain-level app.

A collection of settings that update the current configuration for the RStudioServerPro Domain-level app.

Describes the S3 data source.

Specifies the S3 location of ML model data to deploy.

The Amazon Simple Storage (Amazon S3) location and and security configuration for OfflineStore.

The metric for a scaling policy.

An object where you specify the anticipated traffic pattern for an endpoint.

Configuration details about the monitoring schedule.

An array element of SecondaryStatusTransitions for DescribeTrainingJob (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeTrainingJob.html).

A step selected to run in selective execution mode.

The selective execution configuration applied to the pipeline run.

The ARN from an execution of the current pipeline.

Specifies options for sharing Amazon SageMaker Studio notebooks.

A configuration for a shuffle option for input data in a channel.

Specifies an algorithm that was used to create the model package.

A list of algorithms that were used to create a model package.

The application settings for a Code Editor space.

The space's details.

The settings for the JupyterLab application within a space.

A collection of space settings.

Specifies summary information about the space settings.

Specifies a limit to how long a model training job or model compilation job can run.

Details of the Amazon SageMaker Studio Lifecycle Configuration.

Describes a work team of a vendor that does the a labelling job.

The collection of settings used by an AutoML job V2 for the tabular problem type.

A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

The TensorBoard app settings.

Configuration of storage locations for the Amazon SageMaker Debugger TensorBoard output data.

The collection of settings used by an AutoML job V2 for the text generation problem type.

Used to set feature group throughput configuration.

Active throughput configuration of the feature group.

The new throughput configuration for the feature group.

The collection of settings used by an AutoML job V2 for the time-series forecasting problem type.

Time series forecast settings for the SageMaker Canvas application.

Defines the traffic routing strategy during an endpoint deployment to shift traffic from the old fleet to the new fleet.

TrainingJob is the Schema for the TrainingJobs API +kubebuilder:object:root=true +kubebuilder:subresource:status +kubebuilder:printcolumn:name="FAILURE-REASON",type=string,priority=1,JSONPath=`.status.failureReason` +kubebuilder:printcolumn:name="SECONDARY-STATUS",type=string,priority=0,JSONPath=`.status.secondaryStatus` +kubebuilder:printcolumn:name="STATUS",type=string,priority=0,JSONPath=`.status.trainingJobStatus`.

Contains information about a training job.

Defines the input needed to run a training job using the algorithm.

TrainingJobList contains a list of TrainingJob +kubebuilder:object:root=true.

TrainingJobSpec defines the desired state of TrainingJob.

TrainingJobStatus defines the observed state of TrainingJob.

The numbers of training jobs launched by a hyperparameter tuning job, categorized by status.

Metadata for a training job step.

Provides summary information about a training job.

Defines how the algorithm is used for a training job.

Describes the location of the channel data.

Describes the input source of a transform job and the way the transform job consumes it.

TransformJob is the Schema for the TransformJobs API +kubebuilder:object:root=true +kubebuilder:subresource:status +kubebuilder:printcolumn:name="FAILURE-REASON",type=string,priority=1,JSONPath=`.status.failureReason` +kubebuilder:printcolumn:name="STATUS",type=string,priority=0,JSONPath=`.status.transformJobStatus`.

A batch transform job.

Defines the input needed to run a transform job using the inference specification specified in the algorithm.

TransformJobList contains a list of TransformJob +kubebuilder:object:root=true.

TransformJobSpec defines the desired state of TransformJob.

TransformJobStatus defines the observed state of TransformJob.

Metadata for a transform job step.

Provides a summary of a transform job.

Describes the results of a transform job.

Describes the resources, including ML instance types and ML instance count, to use for transform job.

Describes the S3 data source.

The properties of a trial as returned by the Search (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_Search.html) API.

The properties of a trial component as returned by the Search (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_Search.html) API.

A summary of the metrics of a trial component.

A short summary of a trial component.

A summary of the properties of a trial component.

A summary of the properties of a trial.

Time to live duration, where the record is hard deleted after the expiration time is reached; ExpiresAt = EventTime + TtlDuration.

The job completion criteria.

Metadata for a tuning step.

Provided configuration information for the worker UI for a labeling job.

Information about the user who created or modified an experiment, trial, trial component, lineage group, project, or model card.

UserProfile is the Schema for the UserProfiles API +kubebuilder:object:root=true +kubebuilder:subresource:status +kubebuilder:printcolumn:name="STATUS",type=string,priority=0,JSONPath=`.status.status`.

The user profile details.

UserProfileList contains a list of UserProfile +kubebuilder:object:root=true.

UserProfileSpec defines the desired state of UserProfile.

UserProfileStatus defines the observed state of UserProfile.

A collection of settings that apply to users in a domain.

Specifies a production variant property type for an Endpoint.

Configuration for your vector collection type.

Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to.

Status and billing information about the warm pool.

A single private workforce, which is automatically created when you create your first private work team.

The workspace settings for the SageMaker Canvas application.

Provides details about a labeling work team.