Designing an AWS Well Architected Framework for AI/ML
summary
Shows how to build a production ML pipeline on AWS using SageMaker notebooks, S3, Athena and Glue for discovery, CodeCommit/CodeBuild/CodePipeline for versioned CI/CD, and SageMaker model monitor plus CloudWatch/CloudTrail for alerts and auditing. Recommends training-once artifacts in S3, blue‑green or canary endpoint rollouts, spot training to cut costs, and SageMaker Neo/Greengrass for edge inference. Claims automated retrain triggers on data drift and illustrates reproducible recovery by versioning models, code, features and training inputs.
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chapters (7)
- 0:00Intro
- 0:24What is AIML
- 4:07Early ML Development Lifecycle
- 12:04Reliability
- 15:38Performance Efficiency
- 20:02Cost Optimization
- 24:28Outro
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