2018/07/30 - 1 updated api methods
Changes Added SecondaryStatusTransitions to DescribeTrainingJob to provide more visibility into SageMaker training job progress and lifecycle.
2018/07/17 - 4 new api methods
Changes Amazon SageMaker has added the capability for customers to run fully-managed, high-throughput batch transform machine learning models with a simple API call. Batch Transform is ideal for high-throughput workloads and predictions in non-real-time scenarios where data is accumulated over a period of time for offline processing.
2018/07/05 - 3 updated api methods
Changes Amazon SageMaker NotebookInstances supports 'Updating' as a NotebookInstanceStatus. In addition, DescribeEndpointOutput now includes Docker repository digest of deployed Model images.
2018/06/04 - 5 new 1 updated api methods
Changes Amazon SageMaker has added the ability to run hyperparameter tuning jobs. A hyperparameter tuning job will create and evaluate multiple training jobs while tuning algorithm hyperparameters, to optimize a customer specified objective metric.
2018/04/30 - 4 updated api methods
Changes SageMaker has added support for VPC configuration for both Endpoints and Training Jobs. This allows you to connect from the instances running the Endpoint or Training Job to your VPC and any resources reachable in the VPC rather than being restricted to resources that were internet accessible.
2018/04/04 - 8 updated api methods
Changes SageMaker is now supporting many additional instance types in previously supported families for Notebooks, Training Jobs, and Endpoints. Training Jobs and Endpoints now support instances in the m5 family in addition to the previously supported instance families. For specific instance types supported please see the documentation for the SageMaker API.
2018/03/15 - 5 new 4 updated api methods
Changes This release provides support for ml.p3.xlarge instance types for notebook instances. Lifecycle configuration is now available to customize your notebook instances on start; the configuration can be reused between multiple notebooks. If a notebook instance is attached to a VPC you can now opt out of internet access that by default is provided by SageMaker.
2018/01/18 - 4 updated api methods
Changes CreateTrainingJob and CreateEndpointConfig now supports KMS Key for volume encryption.
2017/11/29 - 29 new api methods
Changes Amazon SageMaker is a fully-managed service that enables data scientists and developers to quickly and easily build, train, and deploy machine learning models, at scale.