Feature Store Meetup #13 with Scribble Data

Feature Stores for Sub-ML

Feature stores have been traditionally designed for complex ML applications (Big-ML) that normally assume clear and high value propositions, long lead times, skilled staff, and advanced methods.

Sub-ML is a space of mid-complexity ML applications where there is higher uncertainty in terms of value, methods used, available staffing, and speed is critical. Sub-ML is interesting and needs to be understood because we observe that this space is rapidly growing across organizations and functions. The difference in the nature of the problem spaces leads to a feature store design that is different from many of the existing design points.

In this talk, Venkata will expand on Scribble Data's observations about the problem space, design constraints, and the thinking behind their product.

Venkata Pingali - Co-Founder & CEO at Scribble Data
Dr. Venkata Pingali is the Founder and CEO of Scribble Data, a venture-backed enterprise MLOps (Feature Store) company with offices in Toronto & Bangalore. Dr. Pingali is well regarded in the community as an expert, and was featured in a number of forums on data, privacy, technology, and startups. He has a BTech from IIT Mumbai and PhD from USC.

--

This global meetup is organized by https://featurestore.org, a voluntary educational program concerned with disseminating information about Feature Stores for machine learning.

If you are interested in talking in one of our upcoming meetups, you can submit your talk proposal through our website.