Practical Weak Supervision - Doing More with Less Data

Getting quality labeled data for supervised learning is an important step towards training performant machine learning models. In many real-world projects, getting labeled data often takes up significant amount of time. Weak Supervision is emerging as an important catalyst towards enabling data science team to fuse insights from labeling functions in order to produce weakly labeled datasets that can be used as inputs for machine learning and deep learning tasks.
In this book, authors Amit Bahree, Senja Filipi, and Wee Hyong Tok from Microsoft help those working on machine learning projects face the typical challenges of getting good, quality labeled data for their projects. Readers will learn:

• The lifecycle of enterprise machine learning projects, and the emerging area of weak supervision
• How to use Snorkel for data labeling
• How to use the weakly labeled dataset for Natural Language Processing
• How to use the weakly labeled dataset for Computer Vision

About the author

is a product and AI leader with a background in product management, machine learning/deep learning, research, and working on complex technical engagements with customers. Over the years, he has demonstrated that the early thought-leadership whitepapers he wrote on tech trends have become reality, and are deeply integrated into many products. Wee Hyong has worn many hats in his career—developer, program/product manager, data scientist, researcher, and strategist, and his range of experience has given him unique superpowers to lead and define the strategy for high-performing data and AI innovation teams.

Summary

Most data scientists and engineers today rely on quality labeled data to train machine learning models. But building a training set manually is time-consuming and expensive. There's a more practical approach. In this book, Wee Hyong Tok, Amit Bahree, and Senja Filipi show you how to create products using weakly supervised learning models.

Product details

• Authors: Amit Bahree, Bahree Amit, Senja Filipi, Wee Hyong, Wee Hyong Tok, Russell Jurney, Wee Hyong Tok, Wee Hyong Tok
• Publisher: O'Reilly
• Languages: English
• Product format: Paperback / Softback
• Released: 30.11.2021
• EAN: 9781492077060
• ISBN: 978-1-4920-7706-0
• Dimensions: 178 mm x 233 mm x 11 mm
• Weight: 340 g
• Subjects: Natural sciences, medicine, IT, technology > IT, data processing > IT; machine learning, Artificial Intelligence, BUSINESS & ECONOMICS / Operations Research, COMPUTERS / Machine Theory, Artificial Intelligence (AI), Enterprise Software, Mathematical theory of computation, Operational research, COMPUTERS / Artificial Intelligence / General, COMPUTERS / Data Science / Machine Learning, COMPUTERS / Business & Productivity Software / General