July 2021
13h 34m
English
Overview
Most machine learning systems that are deployed in the world today learn from human feedback. However, most machine learning courses focus almost exclusively on the algorithms, not the human-computer interaction part of the systems. This can leave a big knowledge gap for data scientists working in real-world machine learning, where data scientists spend more time on data management than on building algorithms. Human-in-the-Loop Machine Learning is a practical guide to optimizing the entire machine learning process, including techniques for annotation, active learning, transfer learning, and using machine learning to optimize every step of the process.
About the Technology
Machine learning applications perform better with human feedback. Keeping the right people in the loop improves the accuracy of models, reduces errors in data, lowers costs, and helps you ship models faster.
About the Book
Human-in-the-Loop Machine Learning lays out methods for humans and machines to work together effectively. You’ll find best practices on selecting sample data for human feedback, quality control for human annotations, and designing annotation interfaces. You’ll learn to create training data for labeling, object detection, and semantic segmentation, sequence labeling, and more. The book starts with the basics and progresses to advanced techniques like transfer learning and self-supervision within annotation workflows.
What's Inside
About the Reader
About the Author
Robert (Munro) Monarch is a data scientist and engineer who has built machine learning data for companies such as Apple, Amazon, Google, and IBM. He holds a PhD from Stanford.
Robert holds a PhD from Stanford focused on Human-in-the-Loop machine learning for healthcare and disaster response, and is a disaster response professional in addition to being a machine learning professional. A worked example throughout this text is classifying disaster-related messages from real disasters that Robert has helped respond to in the past.
Quotes
An excellent reference for learning about active learning from both practical and theoretical perspectives.
- Sayak Paul, PyimageSearch
This book is a must-read for all practitioners who want to integrate active learning in their development cycles. Well written, good coverage, and filled with practical examples.
- Vidhya Vinay, Streamlingo.ai
Very apt real-world examples. I will recommend this book to my colleagues.
- Rajesh Kumar R S, Mindtree
The best reference about supervised machine learning from a real expert and practitioner.
- Michal Rutka, MQL Service
About the Technology
Machine learning applications perform better with human feedback. Keeping the right people in the loop improves the accuracy of models, reduces errors in data, lowers costs, and helps you ship models faster.
About the Book
Human-in-the-Loop Machine Learning lays out methods for humans and machines to work together effectively. You’ll find best practices on selecting sample data for human feedback, quality control for human annotations, and designing annotation interfaces. You’ll learn to create training data for labeling, object detection, and semantic segmentation, sequence labeling, and more. The book starts with the basics and progresses to advanced techniques like transfer learning and self-supervision within annotation workflows.
What's Inside
- Identifying the right training and evaluation data
- Finding and managing people to annotate data
- Selecting annotation quality control strategies
- Designing interfaces to improve accuracy and efficiency
About the Reader
About the Author
Robert (Munro) Monarch is a data scientist and engineer who has built machine learning data for companies such as Apple, Amazon, Google, and IBM. He holds a PhD from Stanford.
Robert holds a PhD from Stanford focused on Human-in-the-Loop machine learning for healthcare and disaster response, and is a disaster response professional in addition to being a machine learning professional. A worked example throughout this text is classifying disaster-related messages from real disasters that Robert has helped respond to in the past.
Quotes
An excellent reference for learning about active learning from both practical and theoretical perspectives.
- Sayak Paul, PyimageSearch
This book is a must-read for all practitioners who want to integrate active learning in their development cycles. Well written, good coverage, and filled with practical examples.
- Vidhya Vinay, Streamlingo.ai
Very apt real-world examples. I will recommend this book to my colleagues.
- Rajesh Kumar R S, Mindtree
The best reference about supervised machine learning from a real expert and practitioner.
- Michal Rutka, MQL Service
Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Listen now
Unlock full access
More than 5,000 organizations count on O’Reilly
O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.