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 9 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.
- Write the ML PRD and pipeline contracts first, choose tools second, write code third — teams that invert this order spend months integrating incompatible systems and debugging failures they have no visibility into.
- Treat pipeline deploys like software releases and model deploys like controlled experiments — separate the two lanes, enforce gates on each, and most ML production incidents become preventable.
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.
Project Planning, Tech Stack & Architecture Design
Write the ML PRD and pipeline contracts first, choose tools second, write code third — teams that invert this order spend months integrating incompatible systems and debugging failures they have no visibility into.
Environments, Branching, CI/CD & Deployments
Treat pipeline deploys like software releases and model deploys like controlled experiments — separate the two lanes, enforce gates on each, and most ML production incidents become preventable.
Data Sourcing, Discovery & Understanding
Write the data requirements document before touching a dataset — teams that start with data hunting instead of data specification build models on signals that don't exist at serving time, labels that leak from the future, and sources they'll never be able to audit or trust.
Data Cataloging, Metadata & Lineage
A data catalog is not a documentation project — it is the operational backbone that makes ML models auditable, reproducible, and debuggable. Teams that skip cataloging discover its necessity the hard way: a model regression they cannot root-cause, a compliance audit they cannot pass, or a critical dataset deletion they cannot undo.
Data Engineering & Batch Pipelines
Batch pipelines are production software systems, not ETL scripts. The difference between a reliable batch pipeline and a fragile one is whether it is idempotent, whether it tracks three distinct time semantics, whether it gates on validated data quality, and whether it was designed with a production-readiness checklist from day one.
Streaming Data Pipelines & Real-Time ML Infrastructure
Streaming ML hinges on event time versus processing time, windowing, and watermarks; get them wrong and features leak or arrive late.