Learn the skills necessary to design, build, and deploy applications powered by machine learning. 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 with little or no ML experience will learn the tools, best practices, and challenges involved in building a real-world ML application step-by-step.
Author Emmanuel Ameisen, who worked as a data scientist at Zipcar and led Insight Data Science's AI program, demonstrates key ML concepts with code snippets, illustrations, and screenshots from the book's example application.
The first part of this guide shows you how to plan and measure success for an ML application. Part II shows you how to build a working ML model, and Part III explains how to improve the model until it fulfills your original vision. Part IV covers deployment and monitoring strategies.
This book will help you:
Determine 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 model and address performance bottlenecks
Deploy and monitor models in a production environment
256 pages, Paperback
Published February 11, 2020
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This book is not about the fad of machine learning but what else comes with it.
In current times, ML has become something which most of us think that we need to create some model, learn some algorithm and it will be done, which is not true in the real world.
This book explains what are the different things that come with a simple problem which everyone thinks can be solved with ML algorithms. I think everyone who wants to work on machine learning projects should read this book. It's a good and quick read and can be referred back to again and again.
2 reviews 1 followerThis is not the typical machine learning book that I have seen so far that covers the various learning algorithms and models. It takes the reader from a product idea, with a practical example, in a walkthrough style of writing, all the way to the deployment. It provides the full source code of the application built and it is discussed in the book chapters. As it states in the book, it is not an introductory book to machine learning but requires some basic knowledge in the area. Reading the book - Building Machine Learning powered application, going from idea to product - feels like working in a real world project with a mentor giving advice on little details that could go wrong and how to be fix it.
In addition to the practical examples the author provides in the discussion of the individual topics, the interviews in the book also reinforce the concepts using experiences of various professionals from multiple big organisations. The references cited in the book also supplement the material discussed. For me, this book is what has been missing to transition from playing with existing datasets to experiment with machine learning models in class or other books to the practical world where you validate the idea, collect and prepare the data, build a complete pipeline, evaluate your model, deploy and monitor the output of your model to refine it in production and iterate.
In general, the book is perfect reference for individuals looking a carrier in companies building ML powered applications or companies that are planning to integrate ML in their product offering. I am sure I have to get back and refer to it when I am going to work on my project soon.
