Thank you for purchasing the MEAP edition of Human-in-the-Loop Machine Learning.
This book is for people trying to answer one of the most important problems in technology: how should humans & machines work together to solve tasks? Machine Learning is a very hot topic and most Machine Learning models are guided by human examples. But most Machine Learning texts and courses only focus on one part of the problem: building Machine Learning models. There are additional key pieces in most deployed Machine Learning systems, including selecting the right data for humans to review and strategies for creating training data.
In the course of my career, I’ve worked on Human-in-the-Loop Machine Learning for disaster response, self-driving vehicles, music recommendations, online commerce, voice-enabled devices, translation, and a wide range of other use cases. I see the variety of innovative new use cases growing daily. I’m excited to share strategies and techniques that will help you develop systems that combine human and machine intelligence for any task that you are trying to solve. In this book, we’ll look at best practices for selecting data to annotate for Machine Learning; for getting accurate annotations as efficiently as possible; and for leveraging more advanced techniques in Transfer Learning and Active Learning.