The company, whose focus is on "making AI practical," also announced its end-to-end Machine Learning (ML) platform, Snorkel Flow. Snorkel Flow, says the company, enables developers and non-developers alike to build and deploy AI applications in a fraction of the time by programmatically labeling and managing the training data that fuels modern AI.
Manually managing, building, and labeling large training datasets has emerged as one of the most significant bottlenecks to the adoption of AI, with the process often requiring weeks or months of manual effort for each application. The Snorkel AI founding team say they saw how this training data issue was becoming the key problem in AI, and after spending four years developing and deploying technology to solve this problem with companies like Google, Intel and Apple, and organizations like DARPA and Stanford Hospital, they spun out to launch Snorkel AI and build an end-to-end platform that made this technology accessible to all enterprises.
"Despite spending billions of dollars on AI, few organizations have been able to use it as widely and effectively as they want to," says Alex Ratner, CEO of Snorkel AI. "This is because available solutions either ignore the most important part of AI today – the labeled training data that fuels modern approaches – or rely on armies of human labelers to produce it. Our end-to-end platform, Snorkel Flow, focuses on a new programmatic approach to the training data that enables enterprises to use AI where they couldn't before."
With Snorkel Flow, users develop "labeling functions," or rules or heuristics, and other programmatic operators, which Snorkel Flow automatically integrates to train state-of-the-art machine learning models. Users can easily improve and adapt these models just by editing their programmatic training data in Snorkel Flow's guided interface. Snorkel Flow, says the company, is especially impactful for the many sectors where data is extremely difficult to label and manage by hand, as it requires expensive subject matter