MLREL05-BP02 Create a recoverable endpoint with a managed version control strategy
Establish a fully recoverable system for model prediction endpoints by implementing proper version control and lineage tracking for components that generate these endpoints.
Desired outcome: You have a robust infrastructure where components related to model deployment, including model artifacts, container images, and endpoint configurations, are version controlled and traceable. You can recover quickly from issues by identifying and reverting to previous stable versions, and you can audit the full lineage of model deployments for governance and regulatory requirements.
Common anti-patterns:
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Storing model artifacts without proper versioning.
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Using deployment processes only when needed without infrastructure as code.
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Failing to track dependencies between model artifacts, containers, and configurations.
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Not maintaining a centralized registry for models.
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Relying on manual processes for endpoint recovery.
Benefits of establishing this best practice:
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Reduces recovery time when endpoint issues occur.
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Improves auditability and adherence through comprehensive lineage tracking.
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Enhances collaboration between data scientists and operations teams.
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Improves the consistency and reliability of model deployments.
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Enables reproduction of model endpoints exactly as they were at specific points in time.
Level of risk exposed if this best practice is not established: High
Implementation guidance
Machine learning model endpoints represent the interface where your business delivers value from AI/ML investments. Verifying that these endpoints are recoverable is critical for business continuity. Recoverability depends on having comprehensive version control for components involved in creating the endpoint.
A properly implemented MLOps framework for endpoint recoverability tracks not just the model artifacts, but components that influence the prediction service—training data, feature transformations, container definitions, and infrastructure configurations. When incidents occur, you need to understand the complete lineage of your model endpoint, including which data trained the model, which code created the container, and which configurations defined the infrastructure.
By implementing proper version control and lineage tracking, you create a system that is both resilient to failures and compatible with governance requirements. You can trace exactly how each endpoint was created and recreate it precisely when needed.
Implementation steps
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Implement MLOps with Amazon SageMaker AI Pipelines and Projects. Amazon SageMaker AI Pipelines
automates ML workflows by providing a service for building, running, and managing ML pipelines. It handles every step from data preparation to model deployment in a versioned, predictable manner. -
Implement a model registry system. Use Amazon SageMaker AI Model Registry to catalog your models for production. The registry tracks model versions and their approval status, creating a system of record for models in your organization. Define a clear approval workflow for moving models from development to production for proper governance at each stage. For each model, register metadata including performance metrics, training datasets, and intended use cases.
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Track experiments with SageMaker AI MLflow. MLflow in SageMaker AI allows you to create, manage, analyze, and compare experiments. This way, data scientists can view and track the experiments for the current project. Each experiment logs every metric and hyperparameter automatically, along with model artifacts and dataset information.
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Use infrastructure as code (IaC) tools. Define and build your infrastructure, including model endpoints, using AWS CloudFormation
or AWS CDK . IaC makes your infrastructure version controlled, repeatable, and able to be reverted to previous states if needed. Store your infrastructure code in git repositories alongside your model code, creating a unified version history. This approach reduces configuration drift between environments and verifies that your production deployment exactly matches your tested configuration. -
Store containers in Amazon Elastic Container Registry. Use Amazon ECR to version and store the Docker containers that serve your models. Amazon ECR automatically creates version hashes for containers as you update them, enabling rollbacks to previous versions. Implement image scanning to detect security vulnerabilities, and apply lifecycle policies to manage older versions of your containers.
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Implement automated testing and deployment pipelines. Create CI/CD pipelines using AWS CodePipeline
to automate the testing and deployment of your models. These pipelines should validate models before deployment, deploy infrastructure changes through CloudFormation, and update model endpoints with minimal downtime. Integrate automated quality checks to avoid problematic models from reaching production. -
Configure automated backups and recovery processes. Establish automated backup procedures for your model artifacts, container images, and endpoint configurations. Use Amazon S3 versioning for model artifacts and AWS Backup
to protect configuration data. Document and test recovery procedures regularly to verify that they work when needed.
Resources
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