Introduction to programmatic labeling
Watch on demand
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 at the 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.
- A short demonstration of data-centric AI development in Snorkel Flow powered by programmatic labeling, including automated suggestions, guidance, and integration with foundation models such as GPT-3.
Presented by
Braden Hancock
Co-founder
Snorkel AI
Braden is a co-founder and Head of Technology at Snorkel AI. Before Snorkel, Braden spent four years developing new programmatic approaches for efficiently labeling, augmenting, and structuring training data with the Stanford AI Lab, Facebook, and Google. Prior to that, he performed NLP and ML research at Johns Hopkins University and MIT Lincoln Laboratory and earned a B.S. in Mechanical Engineering from Brigham Young University.