Machine Learning Systems: Designs that scale

by Jeff Smith

Summary Machine Learning Designs that scale is an example-rich guide that teaches you how to implement reactive design solutions in your machine learning systems to make them as reliable as a well-built web app. Foreword by Sean Owen, Director of Data Science, Cloudera Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology If you’re building machine learning models to be used on a small scale, you don't need this book. But if you're a developer building a production-grade ML application that needs quick response times, reliability, and good user experience, this is the book for you. It collects principles and practices of machine learning systems that are dramatically easier to run and maintain, and that are reliably better for users. About the Book Machine Learning Designs that scale teaches you to design and implement production-ready ML systems. You'll learn the principles of reactive design as you build pipelines with Spark, create highly scalable services with Akka, and use powerful machine learning libraries like MLib on massive datasets. The examples use the Scala language, but the same ideas and tools work in Java, as well. What's Inside Working with Spark, MLlib, and Akka Reactive design patterns Monitoring and maintaining a large-scale system Futures, actors, and supervision About the Reader Readers need intermediate skills in Java or Scala. No prior machine learning experience is assumed. About the Author Jeff Smith builds powerful machine learning systems. For the past decade, he has been working on building data science applications, teams, and companies as part of various teams in New York, San Francisco, and Hong Kong. He blogs ( //medium.com/@jeffksmithjr), tweets (@jeffksmithjr), and speaks (www.jeffsmith.tech/speaking) about various aspects of building real-world machine learning systems. Table of Contents PART 1 - FUNDAMENTALS OF REACTIVE MACHINE LEARNING Learning reactive machine learning Using reactive tools PART 2 - BUILDING A REACTIVE MACHINE LEARNING SYSTEM Collecting data Generating features Learning models Evaluating models Publishing models Responding PART 3 - OPERATING A MACHINE LEARNING SYSTEM Delivering Evolving intelligence  

Genres: Computer Science, Technology

226 pages, Kindle Edition

Published May 21, 2018

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Reviews

[Zdeněk Tisoň · Rating 3 out of 5]

This book describes all steps which needs to be done to have machine learning feature in your system. Steps are described briefly and simple implementation on Scala stack is provided.
I think that book should provide more details also some real world issues and solutions could be provided.
According to reactivity its not only about Spark, Akka. I miss reactive ml models learning, data processing by streams and so on.

[Mikhail Filatov · Rating 1 out of 5]

This book has very little relation to "machine learning" : except a couple of chapters it's really about software engineering topics the author likes: functional programming with Scala, actors in Akka, etc., etc. Can they be used in "machine learning system" - of course. Are they really needed to build one? Not at all.
It seems to be an extension of the blog post where the social app for dogs was used as an example, so in every chapter the "case study" involves some animals: lions, turtles, etc. You feel like reading a book for children at times.

[Raja Pakrashi · Rating 4 out of 5]

Loved this short book. Rather than focusing on the working of Machine learning it focuses a lot on the system design around Machine Learning applications which in the real world determines the success of an organization ML strategy. Highlight of the book was the discussion around distributed databases to effectively collect data using the animal kingdom analogy.

[Bart Frenk]

Haven't read it in full, but this seems to be a fairly shallow book. I also thought the supposedly humorous example detract from the message. To me, they were more distracting than useful.