AWS AI Course - AI With AWS Online Training Hyderabad

1. CI/CD for Machine Learning with Amazon SageMaker Overview of CI/CD in ML workflows Importance of automation and reproducibility Focus on SageMaker integration. +91-7032290546 www.visualpath.in

2. Understanding CI/CD in ML Title: What is CI/CD for Machine Learning?Content: • CI: Automating data preprocessing, training, testing • CD: Automating model packaging, deployment, and monitoring • Challenges unique to ML: data versioning, model drift, evaluation metrics. +91-7032290546 www.visualpath.in

3. Key Components of the Pipeline • Title: Core Components of ML CI/CDContent: • Source Code Repository (e.g., Git) • Build & Test (e.g., CodeBuild) • Model Training (SageMaker Training Jobs) • Model Evaluation & Approval • Deployment (SageMaker Endpoints / Batch Transform) +91-7032290546 www.visualpath.in

4. AWS Tools Overview Title: AWS Services for CI/CD in MLContent: • SageMaker Pipelines: ML workflow orchestration • CodePipeline: CI/CD orchestration • CodeBuild: Build and test automation • S3: Data/model storage • CloudWatch: Monitoring and logging. +91-7032290546 www.visualpath.in

5. SageMaker Pipelines in Action Title: Building Pipelines with SageMakerContent: • Define steps: preprocessing, training, evaluation, register model • Reusable pipeline components • Trigger pipelines via commits or schedule • Model registry integration for approval flows. +91-7032290546 www.visualpath.in

6. CI/CD Workflow Architecture Title: ML CI/CD Architecture with SageMakerContent: • Code commit triggers CodePipeline • CodeBuild runs unit tests and lints training scripts • SageMaker Pipeline runs full training pipeline • Conditional model registration and approval • Deployment via approved model step. +91-7032290546 www.visualpath.in

7. Automating Deployment • Title: Model Deployment StrategyContent: • Approved models deployed to SageMaker endpoints • A/B testing or canary deployment strategies • Integration with API Gateway or applications • Monitor endpoints via CloudWatch and alarms. +91-7032290546 www.visualpath.in

8. Monitoring and Feedback Loops Title: Post-Deployment MonitoringContent: • Monitor prediction quality and performance • Log data for continuous training triggers • Use SageMaker Model Monitor to detect drift • Close the loop with retraining pipelines. +91-7032290546 www.visualpath.in

9. Best Practices and Takeaways • Title: Key Takeaways and Best PracticesContent: • Automate end-to-end model lifecycle • Use model registry for governance • Isolate and test pipeline steps • Monitor for performance and drift • Keep pipelines modular and reproducible. +91-7032290546 www.visualpath.in

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