Machine Learning Systems: 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.
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 Systems: 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.
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 (https://medium.com/@jeffksmithjr), tweets (@jeffksmithjr), and speaks (www.jeffsmith.tech/speaking) about various aspects of building real-world machine learning systems.
Table of contents
- Brief Table of Contents
- Table of Contents
- About this book
- About the author
- About the cover illustration
- Part 1. Fundamentals of reactive machine learning
- Part 2. Building a reactive machine learning system
- Part 3. Operating a machine learning system
- Getting set up
- A reactive machine learning system
- Phases of machine learning
- List of Figures
- List of Tables
- List of Listings
- Title: Machine Learning Systems: Designs that scale
- Release date: June 2018
- Publisher(s): Manning Publications
- ISBN: 9781617293337
You might also like
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. …
Learn how graph algorithms can help you leverage relationships within your data to develop intelligent solutions …
Practical Statistics for Data Scientists, 2nd Edition
Statistical methods are a key part of data science, yet few data scientists have formal statistical …
Data Science from Scratch, 2nd Edition
To really learn data science, you should not only master the tools—data science libraries, frameworks, modules, …