Machine Learning in Production: From Models to Products
After teaching our Machine Learning in Production class (formerly “Software Engineering for AI-Enabled Systems”) four times, we stupidly made a decision we soon regretted when there were still so many chapters left: We are going to write a book with our collected material.
The full book is and will remain available online under a creative commons license at https://mlip-cmu.github.io/book/. The text here on Medium is a slightly older, less edited version. A final copy will be published by MIT Press soon. For now, cite as: Christian Kästner. Machine Learning in Production: From Models to Products. 2022.
Table of Contents
Part 1: Machine Learning in Production: Going Beyond the Model
Part 2: Requirements Engineering
Part 3: Architecture and design
- Thinking like a software architect
- Quality attributes of ML components
- Deploying a model
- Automating the ML pipeline
- Scaling the system
- Planning for operations
Part 4: Quality assurance
- Quality assurance basics
- Model quality
- Data quality
- Pipeline quality
- System quality
- Testing and experimenting in production
Part 5: Process and Teams
Part 6: Responsible ML Engineering
- Responsible Engineering
- Versioning, provenance, and reproducibility
- Interpretability and explainability
- Safety
- Security and privacy
- Fairness
- Transparency and accountability
All chapters are released under Creative Commons 4.0 BY-SA license.