About this item
Highlights
- 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.
- About the Author: Anthony Sarkis is the lead engineer on Diffgram Training Data Management software and founder of Diffgram Inc.
- 329 Pages
- Computers + Internet,
Description
Book Synopsis
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
About the Author
Anthony Sarkis is the lead engineer on Diffgram Training Data Management software and founder of Diffgram Inc. Prior to that he was a Software Engineer at Skidmore, Owings & Merrill and co-founded DriveCarma.ca.