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From Notebook to Production: The Mental Model
Why ML projects fail in production, reproducibility (data + code + environment + seed), feature stores and training-serving skew, versioning of models / data / features with MLflow and DVC.
Module Content
Why ML Projects Fail in Production
The recurring failure modes. Most aren't model accuracy problems — they're plumbing problems. FIND_VIDEO: search 'why machine learning projects fail production' — recommended channel: Chip Huyen / Made with ML. Aim for 10 min or under.
Recap — The Three Production Gaps
ML in research and ML in production are different disciplines. The gaps between them are predictable.
Reproducibility — Data, Code, Environment, Seed
Four things that determine whether someone else can reproduce your model. Lock all four. FIND_VIDEO: search 'reproducibility machine learning experiments' — recommended channel: Made with ML / DVC. Aim for 10 min or under.
Recap — Building a Reproducible Pipeline
Reproducibility is a discipline, not a tool. The four locks that make your training pipeline reproducible — by you tomorrow and by a teammate next year.
Feature Stores — Why They Matter
The single tool that solves the biggest production ML bug: training-serving skew. FIND_VIDEO: search 'feature store machine learning explained' — recommended channel: Feast / Tecton / Made with ML. Aim for 10 min or under.
Recap — Training/Serving Skew and How to Prevent It
Feature stores aren't just 'a tool' — they're a discipline. The lightweight versions get 80% of the benefit.
Versioning — Models, Data, Features
When something breaks in production, you need to know exactly which model, which data, which features. Versioning is the audit trail. FIND_VIDEO: search 'model versioning MLflow registry' — recommended channel: MLflow / Made with ML. Aim for 10 min or under.
Recap — MLflow, DVC, and Model Registry
Three tools for versioning the three things that matter. Lightweight enough for any team.