| Book | Focus | Depth on MLOps | Year | |------|-------|----------------|------| | Designing ML Systems (Huyen) | End-to-end systems | Very high | 2022 | | ML Engineering (Butcher) | Deployment & patterns | High | 2021 | | Building ML Powered Applications (Ameisen) | Prototype to product | Medium | 2020 | | Reliable ML (Chen, Murphy) | Testing & reliability | High | 2021 (short) | | Introducing MLOps (Treveil) | CI/CD for ML | Medium | 2020 |

Most engineers want to tweak hyperparameters. Huyen forces you to look at the data pipeline first. She discusses:

While many users look for a version of Designing Machine Learning Systems , the best way to utilize Huyen’s insights is through interactive study:

While many students and practitioners search for a PDF of this book to quickly access its insights, the value of Huyen’s work lies not just in specific code snippets, but in a fundamental paradigm shift: