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Case Study
A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a
central model registry, model deployment, and model monitoring.
The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.
The company must implement a manual approval-based workflow to ensure that only approved models can be deployed to production endpoints.
Which solution will meet this requirement?
To implement a manual approval-based workflow ensuring that only approved models are deployed to production endpoints, Amazon SageMaker provides integrated tools such as SageMaker Pipelines and the SageMaker Model Registry.
SageMaker Pipelines is a robust service for building, automating, and managing end-to-end machine learning workflows. It facilitates the orchestration of various steps in the ML lifecycle, including data preprocessing, model training, evaluation, and deployment. By integrating with the SageMaker Model Registry, it enables seamless tracking and management of model versions and their approval statuses.
Implementation Steps:
Define the Pipeline:
Create a SageMaker Pipeline encompassing steps for data preprocessing, model training, evaluation, and registration of the model in the Model Registry.
Incorporate a Condition Step to assess model performance metrics. If the model meets predefined criteria, proceed to the next step; otherwise, halt the process.
Register the Model:
Utilize the RegisterModel step to add the trained model to the Model Registry.
Set the ModelApprovalStatus parameter to PendingManualApproval during registration. This status indicates that the model awaits manual review before deployment.
Manual Approval Process:
Notify the designated approver upon model registration. This can be achieved by integrating Amazon EventBridge to monitor registration events and trigger notifications via AWS Lambda functions.
The approver reviews the model's performance and, if satisfactory, updates the model's status to Approved using the AWS SDK or through the SageMaker Studio interface.
Deploy the Approved Model:
Configure the pipeline to automatically deploy models with an Approved status to the production endpoint. This can be managed by adding deployment steps conditioned on the model's approval status.
Advantages of This Approach:
Automated Workflow: SageMaker Pipelines streamline the ML workflow, reducing manual interventions and potential errors.
Governance and Compliance: The manual approval step ensures that only thoroughly evaluated models are deployed, aligning with organizational standards.
Scalability: The solution supports complex ML workflows, making it adaptable to various project requirements.
By implementing this solution, the company can establish a controlled and efficient process for deploying models, ensuring that only approved versions reach production environments.
Automate the machine learning model approval process with Amazon SageMaker Model Registry and Amazon SageMaker Pipelines
Update the Approval Status of a Model - Amazon SageMaker