Modern AI approaches require massive labeled training datasets to learn from, which traditionally rely on armies of human annotators to label by hand. In Snorkel Flow, users programmatically label, build, and augment training data to drive a radically faster, more flexible, and higher quality end-to-end AI development and deployment process:
*Build and deploy AI in hours without months of hand-labeling
*Iteratively develop, monitor, and adapt to changing inputs and objectives
*Keep data private by avoiding off-premises manual labeling
*Leverage your entire organization's knowledge and data to fuel AI
Snorkel AI tackles a critical blocker to scaling AI: labeled training data. At Stanford AI lab, our founding team developed an automated way to build AI, replacing slow & costly hand-labeling. Today, Chubb, BNY Mellon, & other Fortune 500 companies accelerate AI development by 10-100x using Snorkel.
