Training Data for Machine Learning: Human Supervision from Annotation to Data Science 1st Edition 26988
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 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
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.
- АвторAnthony Sarkis
- КатегоріяШтучний інтелект
- МоваАнглійська
- Рік2023
- Сторінок330
- Формат165х235 мм
- ОбкладинкаМ'яка
- Тип паперуОфсетний
- ІлюстраціїЧорно-білі
- Номер видання1-е вид.
- ЖанрШтучний інтелект
1700 ₴
Відділення Нова Пошта80 ₴
Поштомат Нова Пошта40 ₴
Кур’єр Нова Пошта120 ₴
Відділення УкрПошта50 ₴
Кур’єр за адресою90 ₴
допоможіть тим, хто ще не читав