Machine Learning in Production
Subtitle: From Models to Products
Publisher: The MIT Press
Published: Apr 08, 2025
Language: eng
Format: hardcover
Number of pages: 616
ISBN-13: 9780262049726
First sentence: Machine learning (ML) has enabled incredible advances in the capabilities of software products, allowing us to design systems that would have seemed like science fiction only one or two decades ago, such as personal assistants answering voice prompts, medical diagnoses outperforming specialists, autonomous delivery drones, and tools creatively manipulating photos and video with natural language instructions.
Notes: Source title: Machine Learning in Production: From Models to Products
Table of contents: title: Cover; title: Setting the Stage; title: 1. Introduction; title: 2. From Models to Systems; title: 3. Machine Learning for Software Engineers, in a Nutshell; title: Requirements Engineering; title: 4. When to use Machine Learning; title: 5. Setting and Measuring Goals; title: 6. Gathering Requirements; title: 7. Planning for Mistakes; title: Architecture and Design; title: 8. Thinking like a Software Architect; title: 9. Quality Attributes of ML Components; title: 10. Deploying a Model; title: 11. Automating the Pipeline; title: 12. Scaling the System; title: 13. Planning for Operations; title: Quality Assurance; title: 14. Quality Assurance Basics; title: 15. Model Quality; title: 16. Data Quality; title: 17. Pipeline Quality; title: 18. System Quality; title: 19. Testing and Experimenting in Production; title: Process and Teams; title: 20. Data Science and Software Engineering Process Models; title: 21. Interdisciplinary Teams; title: 22. Technical Debt; title: Responsible ML Engineering; title: 23. Responsible Engineering; title: 24. Versioning, Provenance, and Reproducibility; title: 25. Explainability; title: 26. Fairness; title: 27. Safety; title: 28. Security and Privacy; title: 29. Transparency and Accountability