Description

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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, leaving many companies with unfinished ML projects. 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.

You'll learn how to build natural language processing and computer vision projects using weakly labeled datasets from Snorkel, a spin-off from the Stanford AI Lab. Because so many companies have pursued ML projects that never go beyond their labs, this book also provides a guide on how to ship the deep learning models you build.

• Get up to speed on the field of weak supervision, including ways to use it as part of the data science process
• Use Snorkel AI for weak supervision and data programming
• Get code examples for using Snorkel to label text and image datasets
• Use a weakly labeled dataset for text and image classification
• Learn practical considerations for using Snorkel with large datasets and using Spark clusters to scale labeling

About the Author

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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.