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Sirisha Rella, Technical Product Marketing Manager at Nvidia, recently gave a Lightning Talk presentation on “demystifying” speech AI at Snorkel AI’s Future of Data-Centric AI virtual conference.

Snorkel AI will hold a free Foundation Model Virtual Summit on Tuesday, January 17 where speakers from across the technology industry, including some from Google and Stanford University, will discuss the enterprise use of Foundation Models.

Snorkel Flow debuts a new integration with Microsoft Azure Form Recognizer to help organizations leverage Azure AI services.

Researcher Simran Arora tells Snorkel CEO Alex Ratner how she improved foundation model effectiveness by using “Ask Me Anything”-style questions.

See what’s in our latest Snorkel Flow release and how we’re accelerating data-centric AI development further.

More components in an ML lifecycle are designed to run on autopilot, but some tasks require human-in-the-loop ML, an active research topic that has seen an increasing number of publications in the last 10 years.

A central innovation team at a top US bank wanted to modernize its AI development and data annotation processes in order to create a custom natural language processing (NLP) model that could extract important financial information from 10-Ks. Manually reviewing these documents was taking up valuable time that could be better spent assisting customers. The team used Snorkel Flow’s data-centric AI development process and programmatic labeling to train a customized NLP model that could accurately extract information on interest rate swaps.

MIT’s Technology Review reported this week that workers in Venezuela contracted by outsourced data annotation services provider shared customer data—low-angled pictures intended to be labeled, including one that featured a woman in a private moment in the bathroom—with each other on social media. Programmatic labeling could have minimized this.