v2.2.3

[2.2.3] - 2024-12-09

Fixed

• Issue with anonymized operation metrics collection

Changed

• Migrated source for pipeline configuration file from CodeCommit to S3 bucket
• Patched security vulnerabilities

v2.2.2

Fixed

• Upgrade issue with Lambda Custom Resource Sagemaker layer copy to new blueprints bucket

Updated

• requests updated to 2.32.3

v2.2.1

Updated

• boto3, botocore updated to 1.34.98
• sagemaker-python-sdk updated to 2.218.0 for CVE-2024-34073
• moto testing framework updated to 5.0.6 to remove dependency on python-jose due to CVE
• lambda memory sizes increased to 512
• requests package updated to 2.32.0 due to CVE-2024-35195
• PutBucketTagging permission added to orchestrator lambda iam policy

v2.2.0

Added

• Service Catalog AppRegistry resource to register the CloudFormation templates and underlying resources as an application in both Service Catalog AppRegistry and AWS Systems Manager Application Manager.

Updated

• AWS Cloud Development Kit (CDK) v2.
• Python runtime 3.10.
• Source code folders structure.
• CDK unit tests.
• Python libraries.

v2.1.2

Updated

• The S3 bucket policy on the logging S3 bucket to grant access to the logging service principal (logging.s3.amazonaws.com) for access log delivery.
• Python libraries.

v2.1.1

Updated

• Python runtime 3.10.
• Python libraries.

v2.1.0

Added

• Integration with Amazon SageMaker Model Card and Model Dashboard features to allow customers to perform model card operations. All Amazon SageMaker resources (models, endpoints, training jobs, and model monitors) created by the solution will show up on the SageMaker Model Dashboard.

Fixed

• Missing AWS IAM Role permissions used by the Amazon SageMaker Clarify Model Bias Monitor and Amazon SageMaker Clarify Model Explainability Monitor scheduling jobs.

v2.0.1

[2.0.1] - 2022-08-12

Updated

• The AWS IAM Role permissions with the new naming convention for the temporary Amazon SageMaker endpoints used by the Amazon SageMaker Clarify Model Bias Monitor and Amazon SageMaker Clarify Model Explainability Monitor pipelines.

Fixed

• Environment variables of lambda functions by adding PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python to handle protobuf library breaking changes in versions greater than 3.20.1.

• Empty string image url for the model training pipelines when using Amazon SageMaker Model Registry option.

v2.0.0

Added

• A new pipeline to train Machine Learning (ML) models using Amazon SageMaker built-in algorithms and Amazon SageMaker Training Job.
• A new pipeline to train ML models using Amazon SageMaker built-in algorithms and Amazon Hyperparameter Tuning Job.
• A new pipeline to train ML models using Amazon SageMaker built-in algorithms and
  Amazon SageMaker Autopilot Job.
• Amazon EventBridge Rules
  to notify the solution's administrator(s) about the status of the training jobs.

Updated

• The Amazon Simple Notification Service (SNS)
  Topic, used for pipelines notifications, was moved to the solution's main template.

v1.5.0

Added

• A new pipeline to deploy Amazon SageMaker Clarify Model Bias Monitor. The new pipeline monitors predictions for bias on a regular basis, and generates
  alerts if bias beyond a certain threshold is detected.
• A new pipeline to deploy Amazon SageMaker Clarify Explainability (Feature Attribution Drift) Monitor. The new pipeline helps data scientists and ML engineers
  monitor predictions for feature attribution drift on a regular basis.

Updated

• The solution's name was changed from "AWS MLOps Framework" to "MLOps Workload Orchestrator".