Building Machine Learning Powered Applications

Author: Emmanuel Ameisen
ISBN: 9781492045106

This is a book about the whole process of putting a machine learning model into production, all the engineering that sometimes the data scientists are not aware of or take for granted. It does not explain any algorithms or how to train a model but what to do before and after we have already trained one. I highly recommend getting this book, especially if you are:

The book itself is light on code; there is a GitHub repo that the author uses to showcase an entire use case throughout the whole book. However, it is possible to just read the book without the need to look at the code since it is not the main reason someone would buy this book.

Get the book: Amazon MexicoAmazon EspañaAmazon USAmazon UK

Some quotes

For each successful result published in a research paper or a corporate blog, there are hundreds of reasonable-sounding ideas that have entirely failed.

(...) much of the challenge in ML is similar to one of the biggest challenges in software—resisiting the urge to build pieces that are not needed yet.

An ML program doesn't just have to run-it should produce accurate predictive outputs.

Testing a model's behavior is hard. The majority of code in an ML pipeline is not about the training pipeline or the model itself, however.

In reality, most datasets are a collection of approximate measurements that ignore a larger context.

Some ideas

This page contains affiliate links, which means I may earn a very small commision off your purchase without you paying more. And for that, I thank you.