In the most recent webinar in the SNIA Data, Storage & Networking “AI Stack” webinar series, “From Data to Decisions: Understanding How AI Models Learn,” Cal Foshee and Eric Gamble provided an in-depth look at how computer vision models learn. They shared specific techniques and concrete examples of how to train and test these models, drawing on their many years of experience. In this interview, Cal and Eric, take a deeper dive on the questions around how computer vision really works in production: small models working in sequence, bounding boxes as signal gates, and the relentless pursuit of pattern over assumption. Practical tips cover OCR, rotation, error bucketing, versioning, and why “fail fast” is more than a slogan. And webinar moderator, Erik Smith, explains what the “AI Stack” series is all about. 

SNIA is an industry organization that develops global standards and delivers vendor-neutral education on technologies related to data. In these interviews, SNIA experts on data cover a wide range of topics on both established and emerging technologies.

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