Building Intelligent Systems: A Guide to Machine Learning Engineering - Paperback

by Geoff Hulten (Author)

Shows you how to plan for the various phases of an intelligent system from implementation to operation--what is required to succeed, and when it is time to progress

Teaches you how to craft experiences that collect the data needed to evaluate and grow intelligence

Describes how to design a system to take over model production from humans once the creation process has become reasonably routine

Back Jacket

Produce a fully functioning Intelligent System that leverages machine learning and data from user interactions to improve over time and achieve success.
This book teaches you how to build an Intelligent System from end to end and leverage machine learning in practice. You will understand how to apply your existing skills in software engineering, data science, machine learning, management, and program management to produce working systems.
Building Intelligent Systems is based on more than a decade of experience building Internet-scale Intelligent Systems that have hundreds of millions of user interactions per day in some of the largest and most important software systems in the world.

What You'll Learn:

• Understand the concept of an Intelligent System: What it is good for, when you need one, and how to set it up for success
• Design an intelligent user experience: Produce data to help make the Intelligent System better over time
• Implement an Intelligent System: Execute, manage, and measure Intelligent Systems in practice
• Create intelligence: Use different approaches, including machine learning
• Orchestrate an Intelligent System: Bring the parts together throughout its life cycle and achieve the impact you want

Author Biography

Geoff Hulten is a Machine Learning Scientist and PhD in machine learning. He has managed applied machine learning teams for over a decade, building dozens of Internet-scale Intelligent Systems that have hundreds of millions of interactions with users every day. His research has appeared in top international conferences, received thousands of citations, and won a SIGKDD Test of Time award for influential contributions to the data mining research community that have stood the test of time.