Labeling data manually has become one of the biggest blockers for building AI applications. Hand-labeling data is slow, error-prone, and risks data privacy. Programmatic labeling techniques, developed over half a decade at Stanford AI Lab by the Snorkel AI team, automate the labeling process to generate massive, high-quality datasets in minutes. Programmatic labeling has been proven to cut down AI application development time from weeks or months to minutes or days while keeping data in-house.
This webinar will cover
- The limitations of current labeling strategies and why a lack of labeled training data is holding back AI application development.
- What programmatic labeling is and how it works with examples from financial services, healthcare, and more.
- Applications of programmatic labeling that resulted in more accurate models in dramatically less time allowing models to reach production faster with far less development cost.
Principal Data Scientist
About the presenter
Rajiv Shah is a principal data scientist at Snorkel AI, where his primary focus is on enabling teams to achieve success with AI. He was also a part of data science teams at DataRobot, Caterpillar, and State Farm. Rajiv is a widely recognized speaker on AI, with published research papers, and patents in many domains, including sports analytics, deep learning, and interpretability. He received a Ph.D. and a J.D. from the University of Illinois at Urbana Champaign.