How humans and machines should work together to solve problems is one of the most important questions in technology. However, in machine learning, the accuracy of innovative algorithms often end up with the most attention. But to build the most accurate model quickly you also need clean, relevant, correctly-labeled data for your system to train on. Human-in-the-Loop Machine Learning is a practical guide to optimizing the entire machine learning process, including techniques for annotation, sampling, and even using ML systems to help automate the process.
I've learned a lot of new things about Machine Learning I would never even have considered before.
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For me this book is an eye opening for a new topic: how is possible to improve the results of my ML application in the context of the actual evolution of these technologies?
I am very excited that this book exists. It's unlike any other book I've seen on the subject and addresses some very important topics.
The author does an excellent job describing the techniques and the pros and cons of every presented approach.