How to set alerts for performance degradation. Why This Book is Essential in 2026
But perhaps most importantly, Huyen created and taught the . The lectures from that course became the foundation of the very book we're discussing. The book is a direct translation of the same material she taught to Stanford students, filtered through the lens of her hands-on industry experience.
who want to see their models successfully integrated into real-world applications rather than sitting idle in notebooks.
The system must continue to perform its intended function at the correct level of excellence, even when things go wrong. This means handling bad inputs, database outages, and spikes in traffic without dropping the core user experience. 2. Scalability
Implementing a centralized repository (like Feast or Hopsworks) to allow both offline training and online serving to use the exact same feature definitions, preventing data leakage. Model Development and Training
Identifying "silent failures" like data drift and concept drift, and setting up robust evaluation metrics that reflect real-world performance. Key Takeaways for Engineers & Architects
"One of my biggest takeaways from Chip's book is that most ML failures aren't about the model, they're about bad data pipelines and unnoticed drift."
Ensures that the exact same feature definitions and data transformations are used during both offline training and online serving. Continual Learning and the Myth of Static Models