Case Study: Apple achieves up to 2.9× fewer errors and a 12%+ F1 improvement with Snorkel AI

Apple - Customer Case Study

Apple faced engineering teams working with contradictory or incomplete supervision data while building large-scale applications. To overcome cost, privacy, and cold-start limitations, Apple built an internal system called Overton that leveraged Snorkel AI’s weak supervision framework.

Using Snorkel AI’s framework, Overton scaled labels from 30K to 1M (about 32x), delivered a 12%+ bump in F1, and produced up to 2.9x fewer errors; Snorkel-based applications processed trillions of records and answered billions of queries in multiple languages, showing clear quality and scalability gains.

Apple