Changes Releasing new APIs related to Tuning steps in model building pipelines.
Changes SageMaker model registry now supports up to 5 containers and associated environment variables.
Changes Sagemaker Neo now supports running compilation jobs using customer's Amazon VPC
Changes Enable ml.g4dn instance types for SageMaker Batch Transform and SageMaker Processing
Changes Using SageMaker Edge Manager with AWS IoT Greengrass v2 simplifies accessing, maintaining, and deploying models to your devices. You can now create deployable IoT Greengrass components during edge packaging jobs. You can choose to create a device fleet with or without creating an AWS IoT role alias.
Changes AWS SageMaker - Releasing new APIs related to Callback steps in model building pipelines. Adds experiment integration to model building pipelines.
Changes Amazon SageMaker Autopilot now provides the ability to automatically deploy the best model to an endpoint
Changes Enable retrying Training and Tuning Jobs that fail with InternalServerError by setting RetryStrategy.
Changes Amazon SageMaker Autopilot now supports 1) feature importance reports for AutoML jobs and 2) PartialFailures for AutoML jobs
Changes This feature allows customer to specify the environment variables in their CreateTrainingJob requests.
Changes Adding authentication support for pulling images stored in private Docker registries to build containers for real-time inference.
Changes Support new target device ml_eia2 in SageMaker CreateCompilationJob API
Changes This release adds the ResolvedOutputS3Uri to the DescribeFeatureGroup API to indicate the S3 prefix where offline data is stored in a feature group
Changes Amazon SageMaker now supports core dump for SageMaker Endpoints and direct invocation of a single container in a SageMaker Endpoint that hosts multiple containers.
Changes This release adds expires-in-seconds parameter to the CreatePresignedDomainUrl API for support of a configurable TTL.
Changes Add a new optional FrameworkVersion field to Sagemaker Neo CreateCompilationJob API
Changes This feature allows customers to enable/disable model caching on Multi-Model endpoints.
Changes This feature helps you monitor model performance characteristics such as accuracy, identify undesired bias in your ML models, and explain model decisions better with explainability drift detection.
Changes Amazon SageMaker Pipelines for ML workflows. Amazon SageMaker Feature Store, a fully managed repository for ML features.
Changes This feature enables customers to encrypt their Amazon SageMaker Studio storage volumes with customer master keys (CMKs) managed by them in AWS Key Management Service (KMS).
Changes This release enables customers to bring custom images for use with SageMaker Studio notebooks.
Changes This release enables Sagemaker customers to convert Tensorflow and PyTorch models to CoreML (ML Model) format.
Changes This release adds support for launching Amazon SageMaker Studio in your VPC. Use AppNetworkAccessType in CreateDomain API to disable access to public internet and restrict the network traffic to VPC.
Changes Sagemaker Ground Truth: Added support for a new Streaming feature which helps to continuously feed data and receive labels in real time. This release adds a new input and output SNS data channel.
Changes Amazon SageMaker now supports 1) creating real-time inference endpoints using model container images from Docker registries in customers' VPC 2) AUC(Area under the curve) as AutoPilot objective metric
Changes Sagemaker Ground Truth:Added support for OIDC (OpenID Connect) to authenticate workers via their own identity provider instead of through Amazon Cognito. This release adds new APIs (CreateWorkforce, DeleteWorkforce, and ListWorkforces) to SageMaker Ground Truth service. Sagemaker Neo: Added support for detailed target device description by using TargetPlatform fields - OS, architecture, and accelerator. Added support for additional compilation parameters by using JSON field CompilerOptions. Sagemaker Search: SageMaker Search supports transform job details in trial components.
Changes This release adds the DeleteHumanTaskUi API to Amazon Augmented AI
Changes The new 'ModelClientConfig' parameter being added for CreateTransformJob and DescribeTransformJob api actions enable customers to configure model invocation related parameters such as timeout and retry.
Changes We are releasing HumanTaskUiArn as a new parameter in CreateLabelingJob and RenderUiTemplate which can take an ARN for a system managed UI to render a task.
Changes This release adds a new parameter (EnableInterContainerTrafficEncryption) to CreateProcessingJob API to allow for enabling inter-container traffic encryption on processing jobs.
Changes Change to the input, ResourceSpec, changing EnvironmentArn to SageMakerImageArn. This affects the following preview APIs: CreateDomain, DescribeDomain, UpdateDomain, CreateUserProfile, DescribeUserProfile, UpdateUserProfile, CreateApp and DescribeApp.
Changes Amazon SageMaker now supports running training jobs on ml.g4dn and ml.c5n instance types. Amazon SageMaker supports in "IN" operation for Search now.
Changes This release updates Amazon Augmented AI CreateFlowDefinition API and DescribeFlowDefinition response.
Changes SageMaker UpdateEndpoint API now supports retained variant properties, e.g., instance count, variant weight. SageMaker ListTrials API filter by TrialComponentName. Make ExperimentConfig name length limits consistent with CreateExperiment, CreateTrial, and CreateTrialComponent APIs.
Changes This release adds two new APIs (UpdateWorkforce and DescribeWorkforce) to SageMaker Ground Truth service for workforce IP whitelisting.
Changes SageMaker ListTrialComponents API filter by TrialName and ExperimentName.
Changes You can now use SageMaker Autopilot for automatically training and tuning candidate models using a combination of various feature engineering, ML algorithms, and hyperparameters determined from the user's input data. SageMaker Automatic Model Tuning now supports tuning across multiple algorithms. With Amazon SageMaker Experiments users can create Experiments, ExperimentTrials, and ExperimentTrialComponents to track, organize, and evaluate their ML training jobs. With Amazon SageMaker Debugger, users can easily debug training jobs using a number of pre-built rules provided by Amazon SageMaker, or build custom rules. With Amazon SageMaker Processing, users can run on-demand, distributed, and fully managed jobs for data pre- or post- processing or model evaluation. With Amazon SageMaker Model Monitor, a user can create MonitoringSchedules to automatically monitor endpoints to detect data drift and other issues and get alerted on them. This release also includes the preview version of Amazon SageMaker Studio with Domains, UserProfiles, and Apps. This release also includes the preview version of Amazon Augmented AI to easily implement human review of machine learning predictions by creating FlowDefinitions, HumanTaskUis, and HumanLoops.
Changes Amazon SageMaker now supports multi-model endpoints to host multiple models on an endpoint using a single inference container.
Changes Adds support for the new family of Elastic Inference Accelerators (eia2) for SageMaker Hosting and Notebook Services
Changes Enable G4D and R5 instances in SageMaker Hosting Services
Changes Amazon SageMaker now supports Amazon EFS and Amazon FSx for Lustre file systems as data sources for training machine learning models. Amazon SageMaker now supports running training jobs on ml.p3dn.24xlarge instance type. This instance type is offered as a limited private preview for certain SageMaker customers. If you are interested in joining the private preview, please reach out to the SageMaker Product Management team via AWS Support."
Changes Amazon SageMaker introduces Managed Spot Training. Increases the maximum number of metric definitions to 40 for SageMaker Training and Hyperparameter Tuning Jobs. SageMaker Neo adds support for Acer aiSage and Qualcomm QCS605 and QCS603.
Changes The default TaskTimeLimitInSeconds of labeling job is increased to 8 hours. Batch Transform introduces a new DataProcessing field which supports input and output filtering and data joining. Training job increases the max allowed input channels from 8 to 20.
Changes Workteams now supports notification configurations. Neo now supports Jetson Nano as a target device and NumberOfHumanWorkersPerDataObject is now included in the ListLabelingJobsForWorkteam response.
Changes Amazon SageMaker Automatic Model Tuning now supports random search and hyperparameter scaling.
Changes SageMaker notebook instances now support enabling or disabling root access for notebook users. SageMaker Neo now supports rk3399 and rk3288 as compilation target devices.
Changes SageMaker Training Jobs now support Inter-Container traffic encryption.
Changes Batch Transform Jobs now supports TFRecord as a Split Type. ListCompilationJobs API action now supports SortOrder and SortBy inputs.
Changes Amazon SageMaker Automatic Model Tuning now supports early stopping of training jobs. With early stopping, training jobs that are unlikely to generate good models will be automatically stopped during a Hyperparameter Tuning Job.
Changes Amazon SageMaker now has Algorithm and Model Package entities that can be used to create Training Jobs, Hyperparameter Tuning Jobs and hosted Models. Subscribed Marketplace products can be used on SageMaker to create Training Jobs, Hyperparameter Tuning Jobs and Models. Notebook Instances and Endpoints can leverage Elastic Inference accelerator types for on-demand GPU computing. Model optimizations can be performed with Compilation Jobs. Labeling Jobs can be created and supported by a Workforce. Models can now contain up to 5 containers allowing for inference pipelines within Endpoints. Code Repositories (such as Git) can be linked with SageMaker and loaded into Notebook Instances. Network isolation is now possible on Models, Training Jobs, and Hyperparameter Tuning Jobs, which restricts inbound/outbound network calls for the container. However, containers can talk to their peers in distributed training mode within the same security group. A Public Beta Search API was added that currently supports Training Jobs.
Changes SageMaker now makes the final set of metrics published from training jobs available in the DescribeTrainingJob results. Automatic Model Tuning now supports warm start of hyperparameter tuning jobs. Notebook instances now support a larger number of instance types to include instances from the ml.t3, ml.m5, ml.c4, ml.c5 families.
Changes SageMaker notebook instances can now have a volume size configured.
Changes VolumeKmsKeyId now available in Batch Transform Job
Changes Added an optional boolean parameter, 'DisassociateLifecycleConfig', to the UpdateNotebookInstance operation. When set to true, the lifecycle configuration associated with the notebook instance will be removed, allowing a new one to be set via a new 'LifecycleConfigName' parameter.