Reliable Machine Learning: Applying SRE Principles to ML in Production

This paperback edition of Reliable Machine Learning is in excellent condition, with no visible signs of wear or damage. The cover and pages are intact and clean, and there are no inscriptions or library markings. The book does not include a dust jacket, as it is a paperback.

Reliable Machine Learning: Applying SRE Principles to ML in Production by Cathy Chen, Niall Richard Murphy, Kranti Parisa, D. Sculley, and Todd Underwood, is a comprehensive guide published by O'Reilly Media, Inc. in 2022. This first edition, released in September 2022, offers a deep dive into integrating Site Reliability Engineering (SRE) principles with machine learning systems in production environments. The book provides invaluable insights for data scientists, engineers, and product managers on establishing reliable and accountable ML systems. With contributions from experienced professionals, this edition is distinct for its practical focus and real-world applications. The paperback format, featuring illustrations by Kate Dullea, makes it accessible for both study and reference. As a first edition, it holds particular value for collectors and practitioners interested in the intersection of machine learning and reliability engineering. The book is in excellent condition, with no signs of wear or library markings.