Companies have high hopes for Machine learning and AI to support real-time product offerings, prevent fraud and drive innovation. But there was a catch – training models require labeled data that machines can digest. As data volumes increase, the opportunity to get great ML results rises, but so does the problem of labeling all the data to get that excellent result.
Enter Snorkel AI’s programmatic data labeling and MLops platforms like Snorkel Flow. Today we are interviewing Alex Ratner, one of the founders of Snorkel AI. Snorkel AI evolved from research Alex led as part of his Ph.D. research at Stanford, focused on programmatic data labeling to enable much faster and more accurate ML training and retraining.
Alex is a born teacher who always has enthusiasm for the topic. Today he will share the newest evolutions of the product at Snorkel, shed light on why doing ML well requires programmatic data labeling, and talk about foundation models in actual enterprise settings and generally.
Starting her career as a software developer, Jocelyn Houle is now a Senior Director of Product Management at Securiti.ai, a unified data protection and governance platform. Before that, she was an Operating Partner at Capital One Ventures investing in data and AI startups. Jocelyn has been a founder of two startups and a full life cycle, technical product manager at large companies like Fannie Mae, Microsoft and Capital One. Follow Jocelyn on LinkedIn or Twitter @jocelynbyrne.
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