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v1.2.0
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@aassadza aassadza released this 04 May 18:39
· 44 commits to main since this release
v1.2.0
b2ec92b

Added

  • Two stack deployment options that provision machine learning (ML) pipelines either in a single AWS account, or across multiple AWS accounts for development, staging/test, and production environments.
  • Ability to provide an optional AWS Key Management Service (KMS) key to encrypt captured data from the real-time Amazon SageMaker endpoint, output of batch transform and data baseline jobs, output of model monitor, and Amazon Elastic Compute Cloud (EC2) instance's volume used by Amazon SageMaker to run the solution's pipelines.
  • New pipeline to build and register Docker images for custom ML algorithms.
  • Ability to use an existing Amazon Elastic Container Registry (Amazon ECR) repository, or create a new one, to store Docker images for custom ML algorithms.
  • Ability to provide different input/output Amazon Simple Storage Service (Amazon S3) buckets per pipeline deployment.

Updated

  • The creation of Amazon SageMaker resources using AWS CloudFormation.
  • The request body of the solution's API calls to provision pipelines.
  • AWS SDK to use the solution's identifier to track requests made by the solution to AWS services.
  • AWS Cloud Development Kit (AWS CDK) and AWS Solutions Constructs to version 1.96.0.

Refer to changelog for more information.

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