MLREL04-BP02 Use an appropriate deployment and testing strategy
Select the right deployment and testing strategy for your machine learning models to create smoother transitions to production, minimize disruption, and allow for careful evaluation of model performance before full implementation.
Desired outcome: You can confidently deploy machine learning models to production using strategies that minimize risk and maximize availability. You have established processes to monitor model performance, allowing you to make data-driven decisions about when to roll back to previous versions or roll forward with new updates. Your deployment pipelines include appropriate testing, validation, and metrics collection to improve model quality and performance in production environments.
Common anti-patterns:
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Deploying new models directly to production without testing strategies.
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Lacking version control for model artifacts.
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Not implementing monitoring metrics to evaluate model performance.
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Using the same deployment strategy for models without considering specific use case requirements.
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Failing to plan for rollbacks when model performance degrades.
Benefits of establishing this best practice:
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Minimizes disruption to production services during model updates.
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Enables testing models with real production traffic before full deployment.
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Reduces risk through controlled deployment strategies.
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Improves visibility into model performance.
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Provides better mechanisms for recovering from poor-performing model deployments.
Level of risk exposed if this best practice is not established: High
Implementation guidance
Machine learning models require special consideration when deploying to production environments because their behavior can be complex and sometimes unpredictable. Unlike traditional software where functionality is explicitly coded, ML models learn patterns from data, making it crucial to validate their performance with production data before full deployment.
Different deployment strategies provide varying levels of risk mitigation and testing capabilities. Blue/green deployments allow for instantaneous rollback by maintaining two identical environments. Canary deployments reduce risk by exposing only a small portion of traffic to new models. A/B testing enables you to compare performance metrics between model versions. Shadow deployments let you test new models without affecting user experience by running them alongside the production model but not using their predictions.
Your choice of deployment strategy should be guided by your specific requirements for availability, risk tolerance, and the criticality of the ML application. For high-stakes applications like fraud detection or medical diagnostics, more cautious approaches like canary or shadow deployments may be appropriate. For less critical applications, simpler strategies might suffice.
Implementation steps
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Perform a deployment strategy trade-off analysis. Evaluate your business requirements and risk tolerance to determine which deployment strategies are most appropriate for your ML models. Consider factors like required availability, acceptable downtime, criticality of predictions, and monitoring capabilities. Document your analysis and selection criteria for each model deployment.
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Implement robust model versioning. Establish a system to version model artifacts, including the model itself, preprocessing components, and associated configurations. Use Amazon SageMaker AI Model Registry to catalog models and track their lineage, approval status, and deployment history. Then, you can quickly identify what model version is running and roll back if needed.
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Set up blue/green deployments with SageMaker AI. Implement blue/green deployments for your real-time inference endpoints to maximize availability during updates. SageMaker AI automatically provisions new infrastructure (green fleet) before transitioning traffic from the old infrastructure (blue fleet), providing nearly continuous service. Configure the appropriate traffic shifting mode based on your risk tolerance.
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Configure canary deployments for higher-risk updates. For model updates where you want additional safety, implement canary deployments that route a small percentage of traffic to the new model version first. Use SageMaker AI deployment guardrails to set up canary testing with baking periods to monitor model performance before shifting the remaining traffic.
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Establish linear traffic shifting for granular control. For the most controlled deployments, set up linear traffic shifting in SageMaker AI to gradually move traffic from the blue fleet to the green fleet in multiple steps. Define appropriate step sizes and baking periods between shifts to carefully monitor model behavior at each stage.
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Implement A/B testing for model comparison. When you need to validate that a new model performs better than the existing one, set up A/B testing to compare metrics between versions with real production traffic. Use SageMaker AI A/B testing to route defined percentages of traffic to different model variants and collect performance data.
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Deploy shadow models to reduce the risk of testing. For high-risk applications, consider implementing shadow deployments where the new model runs alongside the production model but doesn't affect customer-facing decisions. This allows you to compare how the new model would have performed using real production data while minimizing the risk.
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Define and implement model performance metrics. Establish clear metrics to evaluate model performance in production, such as prediction accuracy, latency, throughput, and business KPIs. Set up monitoring with Amazon CloudWatch
to track these metrics and create alarms that can run automatic or manual interventions when performance degrades. -
Create automatic rollback mechanisms. Implement automated procedures to roll back to previous model versions when performance metrics indicate problems. Define specific thresholds for metrics that would trigger a rollback, and establish the process for rolling back with minimal disruption.
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Build comprehensive CI/CD pipelines. Integrate your deployment strategies into complete CI/CD pipelines that automate the testing, deployment, and monitoring of models. Use AWS CodePipeline
and AWS CodeDeploy in conjunction with SageMaker AI to create reliable deployment workflows.
Resources
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