Book description
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
Publisher resources
Table of contents
- Foreword by Xuedong Huang
- Foreword by Alex Ratner
- Preface
- 1. Introduction to Weak Supervision
- 2. Diving into Data Programming with Snorkel
- 3. Labeling in Action
- 4. Using the Snorkel-Labeled Dataset for Text Classification
- 5. Using the Snorkel-Labeled Dataset for Image Classification
- 6. Scalability and Distributed Training
- Index
Product information
- Title: Practical Weak Supervision
- Author(s):
- Release date: October 2021
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 9781492077060
You might also like
book
Practical MLOps
Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set …
audiobook
Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems
Data is at the center of many challenges in system design today. Difficult issues need to …
book
Observability Engineering
Observability is critical for building, changing, and understanding the software that powers complex modern systems. Teams …
book
Implementing MLOps in the Enterprise
With demand for scaling, real-time access, and other capabilities, businesses need to consider building operational machine …