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Presentation Transcript
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
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
Key Components of the Pipeline Title: Core Components of ML CI/CD Content: 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
AWS Tools Overview Title: AWS Services for CI/CD in ML Content: 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
SageMaker Pipelines in Action Title: Building Pipelines with SageMaker Content: 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
CI/CD Workflow Architecture Title: ML CI/CD Architecture with SageMaker Content: 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
Automating Deployment Title: Model Deployment Strategy Content: 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
Monitoring and Feedback Loops Title: Post-Deployment Monitoring Content: 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
Best Practices and Takeaways Title: Key Takeaways and Best Practices Content: 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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