All notable changes to this project will be documented in this file.
The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.
- Allows you to provision multiple model monitor pipelines to periodically monitor the quality of deployed Amazon SageMaker's ML models.
- Ability to use an existing S3 bucket as the model artifact and data bucket, or create a new one to store model artifact and data.
- Updates AWS Cloud Development Kit (AWS CDK) and AWS Solutions Constructs to version 1.83.0.
- Updates request body of the Pipelines API's calls.
- Initiates a pre-configured pipeline through an API call or a Git repository
- Automatically deploys a trained model and provides an inference endpoint
- Supports running your own integration tests to ensure that the deployed model meets expectations
- Allows to provision multiple environments to support ML model's life cycle
- Notifies users of the pipeline outcome though SMS or email