Alex Ratner (@ajratner, Co-Founder/CEO @SnorkelAI) talks about Snorkel’s evolution from Stanford AI Labs, the challenges of labeling data for AI modeling, and simplifying how AI applications can be built.

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SHOW NOTES:

Snorkel AI (homepage)
Stanford Spin-Out Snorkel AI Solves a Major Data Problem

Topic 1 - Welcome to the show. Tell us about your background, the origins of the company, and a little bit about the founding team.

Topic 2 - Let’s start by framing the day in the life of a data scientist. There’s raw data, there’s a data sorting/organizing process, there’s model building, there’s results and analysis, and the cycle continues, etc. What parts are solved problems, what parts are commoditized, and where is there still room for improvement?

Topic 3 - Now that we understand today’s AI/ML/DataScience landscape, let’s talk about how Snorkel Flow and automated data labeling is able to evolve those environments

Topic 4 - Application Studio seems like the intersection of Low-Code and Industry-specific templates and the Python toolkit that data scientists understand. Walk us through the mindset of today’s data scientists in how they think about the “developer” part of their jobs.

Topic 5 - What are some of the frequent use-cases or business problem areas that you’ve seen drive early adoption of the Snorkel platform?

Topic 6 - Where do you see Snorkel fitting into the broader ecosystem of AI capabilities that companies may already have in place?

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Automated Data Labeling for AI Apps