Advancing Snorkel from Research to Production
The Snorkel AI founding team started the Snorkel Research Project at Stanford AI Lab in 2015, where we set out to explore a higher-level interface to machine learning through training data. This project was sponsored by Google, Intel, DARPA, and several other leading organizations and the research was represented in over 40 academic conferences such as ACL, NeurIPS, Nature and more.
Snorkel Open Source Research Library was primarily developed from 2015 to 2017 as a prototyping tool. It is a Python library that contains a legacy base class for defining code-based Labeling Functions (LFs) and some early algorithms for combining LF votes, rather than a comprehensive platform supporting the AI development lifecycle.
Snorkel Flow Platform was built by the original creators of the Snorkel Research Project, incorporating years of experience from applying weak supervision and programmatic labeling concepts to real-world ML problems.
In Snorkel Flow, users can label and manage data using code, train models and identify model error modes to iteratively improve them in a rapid, data-centric workflow, using both SDK and no-code interfaces. This shortens the development cycle and improves application quality significantly while being able to manage bias or adapt to any deterioration in production performance.
Snorkel Flow is used by some of the world’s most advanced organizations in banking, insurance, biotech, telecommunications and several government agencies.
Snorkel Flow Platform Interfaces
Snorkel Flow is an enterprise-grade platform that uses the core concepts of Snorkel Research Project. With Snorkel Flow, enterprises are able to build and deploy accurate and adaptable AI applications rapidly.
Encryption (in-transit and at-rest), authentication, and role-based access control (RBAC)