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Building Machine Learning Powered Applications: Going from Idea to Product


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From the Publisher

OReilly Media Inc.OReilly Media Inc.

machine learning, applicationsmachine learning, applications

From the Preface

Over the past decade, Machine Learning (ML) has increasingly been used to power a variety of products such as automated support systems, translation services, recommendation engines, fraud detection models and many, many more.

Surprisingly, there aren’t many resources available to teach engineers and scientists how to build such products. Many books and classes will teach how to train ML models, or how to build software projects, but very few blend both worlds to teach how to build practical applications that are powered by ML.

This book goes through every step of this process, and aims to help you accomplish each of them by sharing a mix of methods, code examples, and advice from me and other experienced practitioners. We’ll cover the practical skills required to design, build, and deploy ML powered applications. The goal of this book is to help you succeed at every part of the ML process.

What This Book Covers

To cover the topic of building applications powered by ML, the focus of this book is concrete and practical. In particular, this book aims to illustrate the whole process of building ML powered applications.

To do so, I will first describe methods to tackle each step in the process. Then, I will illustrate these methods using an example project as a case study. The book also contains many practical examples of ML in industry, and features interviews with professionals that have built and maintained production ML models.

The Entire Process of ML

To successfully serve an ML product to users, you need to do more than simply train a model. You need to thoughtfully translate your product need to an ML problem, gather adequate data, efficiently iterate in between models, validate your results, and deploy them in a robust manner.

Building a model often only represents a tenth of the total workload of an ML project. Mastering the entire ML pipeline is crucial to successfully build projects, succeed at ML interviews, and be a top contributor on ML teams.

A Technical, Practical Case Study

While we won’t be re-implementing algorithms from scratch in C, we will stay practical and technical by using libraries and tools providing higher-level abstractions. We will go through this book building an example ML application together, from the initial idea to the deployed product.

I will illustrate key concepts with code snippets when applicable, as well as figures describing our application. The best way to learn ML is by practicing it, so I encourage you to go through the book reproducing the examples and adapting them to build your own ML powered application.

Real Business Applications

Throughout this book, I will include conversations and advice from ML leaders that have worked on data teams at tech companies such as StitchFix, Jawbone, and FigureEight. These discussions will cover practical advice garnered after building ML applications with millions of users, and correct some popular misconceptions about what makes Data Scientists and Data Science teams successful.


This book assumes some familiarity with programming. I will mainly be using Python for technical examples, and assume that the reader is familiar with the syntax. If you’d like to refresh your Python knowledge, I recommend “The Hitchhiker’s Guide to Python”.

OReilly MediaOReilly Media


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Publisher‏:‎O’Reilly Media; 1st edition (February 11, 2020)
Paperback‏:‎260 pages
Item Weight‏:‎14.7 ounces
Dimensions‏:‎7 x 0.55 x 9.19 inches

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