Training Data for Machine Learning

Your training data has as much to do with the success of your data project as the algorithms themselves--most failures in deep learning systems relate to training data. But while training data is the foundation for successful machine learning, there are few comprehensive resources to help you ace the process. This hands-on guide explains how to work with and scale training data. Data science professionals and machine learning engineers will gain a solid understanding of the concepts, tools, and processes needed to:



Design, deploy, and ship training data for production-grade deep learning applications
Integrate with a growing ecosystem of tools
Recognize and correct new training data-based failure modes
Improve existing system performance and avoid development risks
Confidently use automation and acceleration approaches to more effectively create training data
Avoid data loss by structuring metadata around created datasets
Clearly explain training data concepts to subject matter experts and other shareholders
Successfully maintain, operate, and improve your system