Podcasts Technology The Cloudcast Automated Data Labeling for AI Apps

Automated Data Labeling for AI Apps

Published: June 16, 2021, 5 a.m.

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Alex Ratner (@ajratner, Co-Founder/CEO @SnorkelAI) talks about Snorkel\\u2019s evolution from Stanford AI Labs, the challenges of labeling data for AI modeling, and simplifying how AI applications can be built.\\xa0

SHOW: 523

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SHOW NOTES:

Topic 1 - Welcome to the show. Tell us about your background, the origins of the company, and a little bit about the founding team.\\xa0

Topic 2 - Let\\u2019s start by framing the day in the life of a data scientist. There\\u2019s raw data, there\\u2019s a data sorting/organizing process, there\\u2019s model building, there\\u2019s 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\\u2019s AI/ML/DataScience landscape, let\\u2019s 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\\u2019s data scientists in how they think about the \\u201cdeveloper\\u201d part of their jobs.

Topic 5 - What are some of the frequent use-cases or business problem areas that you\\u2019ve seen drive early adoption of the Snorkel platform?\\xa0

Topic 6 - Where do you see Snorkel fitting into the broader ecosystem of AI capabilities that companies may already have in place?\\xa0

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