Well-Architecture Framework for Machine Learning
summary
Maps the Well-Architected Framework to ML by applying its five pillars—security, cost optimization, operational excellence, reliability, performance efficiency—to concrete ML components: source systems, storage, model registry, dev/train compute and serving on Kubernetes or Databricks. Recommends CI/CD for models, treating a selected model as a binary artifact, continuous deployment, and monitoring throughput not just CPU/RAM. Notes experiments can run a day or a week, so plan costing, identity/key management, and SLOs before hitting production.
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chapters (11)
- 0:00Introduction
- 1:03Framework purpose
- 2:28Five pillars
- 4:33Understanding machine learning basics
- 5:57Machine learning lifecycle explained
- 8:25MLOps pipeline
- 12:35Security in ML
- 15:25Cost optimization
- 16:29Operational excellence
- 17:54Reliability & performance
- 18:55Monitoring & closing
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