Training Data for Machine Learning: Human Supervision from Annotation to Data Scie...

Your training data has as much to do with the success of your data project as the algorithms themselves because most failures in AI systems relate to training data. But while training data is the foundation for successful AI and machine learning, there are few comprehensive resources to help you ace the process. In this hands-on guide, author Anthony Sarkislead engineer for the Diffgram AI training data softwareshows technical professionals, managers, and subject matter experts how to work with and scale training data, while illuminating the human side of supervising machines. Engineering leaders, data engineers, and data science professionals alike will gain a solid understanding of the concepts, tools, and processes they need to succeed with training data.
With this book, you'll learn how to:

Work effectively with training data including schemas, raw data, and annotations

Transform your work, team, or organization to be more AI/ML data-centric

Clearly explain training data concepts to other staff, team members, and stakeholders

Design, deploy, and ship training data for production-grade AI applications

Recognize and correct new training-data-based failure modes such as data bias

Confidently use automation to more effectively create training data

Successfully maintain, operate, and improve training data systems of record