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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".

↑ 2021
2019 ↓
, & Factfulness
 350 Seiten

For months I have wanted to read this book and in January I finally got around to it. Hans Rosling and his co-authors present an intriguing guideline to form a fact-based worldview. In their book, they educate about the actual state the world is in -- and it's dramatically better than what most people think. In addition, they teach about the instincts we use to wrongly think about the world.

In a world where news, social media, others around us (myself included) talk about anecdotal evidence most of the time rather than always knowing the actual proportions of current problems, this book is an amazing guideline. It might become my new go-to reference that I think I will re-read every once in a while. I can whole heartedly recommend this book to everyone.

I do have one nit-pick, though: At one point, they report the fact that tigers, giant pandas and black rhinos are now not more endangered than they were in the 1990s. While this is an encouraging anecdote, here they make the same mistake they want to teach the reader not to make. Why pick 3 examples from the whole data, while the overall state of wild life seems to be getting worse and worse? According to a WWF report, for a lot of species, the populations have drastically decreased in the last 40 years. Maybe the book has already taught me how to questions these factoids before judging the whole, but they could have also picked a more representative data set, I think.

Nonetheless, wow, what a great view of the world. Realistic, yet optimistic (he calls himself a "possibilist", I like that).

Having seen some of Hans Rosling's talks online years ago, then learning about his death after that, and now seeing this book as a summary of his life's work really moved me. I think this is a must read.