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 Sarkis--lead engineer for the Diffgram AI training data software--shows 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 annotationsTransform your work, team, or organization to be more AI/ML data-centricClearly explain training data concepts to other staff, team members, and stakeholdersDesign, deploy, and ship training data for production-grade AI applicationsRecognize and correct new training-data-based failure modes such as data biasConfidently use automation to more effectively create training dataSuccessfully maintain, operate, and improve training data systems of record

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 Sarkis--lead engineer for the Diffgram AI training data software--shows 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 annotationsTransform your work, team, or organization to be more AI/ML data-centricClearly explain training data concepts to other staff, team members, and stakeholdersDesign, deploy, and ship training data for production-grade AI applicationsRecognize and correct new training-data-based failure modes such as data biasConfidently use automation to more effectively create training dataSuccessfully maintain, operate, and improve training data systems of record