Learn the skills necessary to design, build, and deploy applications powered by machine learning (ML). Through the course of this hands-on book, you’ll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managers—including experienced practitioners and novices alike—will learn the tools, best practices, and challenges involved in building a real-world ML application step by step.

Author Emmanuel Ameisen, an experienced data scientist who led an AI education program, demonstrates practical ML concepts using code snippets, illustrations, screenshots, and interviews with industry leaders. Part I teaches you how to plan an ML application and measure success. Part II explains how to build a working ML model. Part III demonstrates ways to improve the model until it fulfills your original vision. Part IV covers deployment and monitoring strategies.

This book will help you:

• •  Define your product goal and set up a machine learning problem
• •  Build your first end-to-end pipeline quickly and acquire an initial dataset
• •  Train and evaluate your ML models and address performance bottlenecks
• •  Deploy and monitor your models in a production environment
  
  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.

Review

"So many books about machine learning skip the hardest parts: refining the problem, debugging models, and deploying to customers. But this book focuses on them so you can move your projects from an idea to making an impact."
- Alexander Gude, Staff Data Scientist, Intuit

"ML models need to be integrated into data products and larger systems to be useful. This is a crucial and hard skill to master. I recommend this excellent book by Emmanuel Ameisen. It covers the entire end-to-end process of building and managing data products."
- Jeremy Howard, Founder & Deep Learning researcher, fast.ai

"If you are looking for practical advice on how to get ML models into production, what could go wrong and what to watch out for, this is your book. I wish I had it 10 years ago. Lots of the lessons I had to learn the hard way."
- Lukas Tencer, Senior Manager, ML at Twitch

"From product thinking, to infrastructure, to the inner workings of machine learning models, this book gamely tackles all of the areas that an MLE needs to be successful. [...] It's so good to FINALLY find a book that discusses deploying and monitoring ML applications and building CI/CD pipelines for ML. Whether you're coming to machine learning engineering by way of data science or by way of software engineering this book holds something for you."
- David Stevens, Software Engineer, Peloton

"It is so full of best practices, it should become mandatory for all ML'ers."
- Darvish Shadravan, Machine Learning, Salesforce

"If you're a practitioner looking to understand the end-to-end process of developing machine learning based products, then this is the book for you. Emmanuel superbly describes each stage of machine learning development, from framing the problem to designing, implementing and operating the models and data pipelines. This book will show you how to build real machine learning systems."
- Luigi Patruno, Founder, MLinProduction.com

"This book was sorely needed in the ML world. There are tons of books out there that detail how ML algorithms work, but this is the first I've come across that explicitly details how to make ML projects work."
- Jon Krohn, Chief Data Scientist, Untapt

"Having worked with Emmanuel as Head of AI at Insight, I vouch for how fantastic his guidance is on this topic."
- Jake Klamka, Founder, Insight Data Science

"the first book I've read that's written the way I write books: build an actual product from end to end. [...] Badass!"
- Russell Jurney, O'Reilly Author

"If you're looking to pick up the skills to break into ML Engineering, I highly recommend this book!"
- Jeremy Karnowski, VP of product, Insight Data Science

About the Author

Emmanuel Ameisen has worked for years as a Data Scientist. He implemented and deployed predictive analytics and machine learning solutions for Local Motion and Zipcar. Recently, Emmanuel has led Insight Data Science's AI program where he oversaw more than a hundred machine learning projects. Emmanuel holds graduate degrees in artificial intelligence, computer engineering, and management from three of France’s top schools.