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, & Machine Learning Design Patterns
 408 Seiten

This was the book I needed to take me one step further: From just knowing "how to train a neural network" to a better understanding of "MLOps", including training workflows, aspects of scalable serving, and reproducibility.

The three authors are employed at Google and it shows in many chapters: The example of choice is always a Google Cloud AI offering or a Tensorflow code snippet. They do make an effort to also mention competitor products and open source alternatives. Because their insight from Google provided them with this wide range of best practices, I won't hold any of this against the book.

The book isn't without its flaws, though. This (recent) first edition has a number of distracting errors (such as misleading numbers in figures and weird code indentation), plus the greyscale print makes it hard to read many of the figures. That fact cost the book its fifth star. A 2nd edition will probably catch up once it irons out these issues.

I for one will keep this book on my shelf for future reference. It's a great collection of best practices to move a team and an organization ahead in terms of "AI readiness".

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