MLOps Production Guide
Workbook-driven systems thinking for shipping ML in production
Learn how to reason about ML systems in production through structured chapters, decision frameworks, and case-study-driven workbook exercises.
Is this course right for you?
Workbook-driven systems thinking for shipping ML in production. Learn how to reason about ML systems in production through structured chapters, decision frameworks, and case-study-driven workbook exercises.
What you'll learn
Across 3 chapters, this course builds practical reasoning you can apply immediately:
- Start with the business outcome, then work top-down: product outcome -> model goal -> decision policy. If those four layers do not line up, no model improvement will save the system.
- MLOps is the operating system for ML in production — code, data, and models all change independently, so you need explicit pipelines, observability, and retraining loops for each.
- Platform choice is an architectural decision — match it to your team's maturity and workflow, not to the vendor's feature list.
Chapters
Chapter 1: ML Problem Framing
Start with the business outcome, then work top-down: product outcome -> model goal -> decision policy. If those four layers do not line up, no model improvement will save the system.
MLOps Blueprint & Operational Strategy
MLOps is the operating system for ML in production — code, data, and models all change independently, so you need explicit pipelines, observability, and retraining loops for each.
MLOps Platforms
Platform choice is an architectural decision — match it to your team's maturity and workflow, not to the vendor's feature list.