Speaker

Venkata Pingali, Co-Founder & CEO, Scribble Data

Abstract

Widespread adoption of machine learning (ML) in industry is still a challenge today due to resource constraints and RoI questions. Production ML approaches today require high skill, rely on large volumes of data, and have long delivery timelines. In this talk, we argue for Sub-ML - a class of ML simpler than traditional ML approaches, often designed to be used in decision support systems, and delivered under tight constraints. Sub-ML, also called as ML-at-reasonable-scale (MLRS) and Analytical ML, covers upto 80% of the ML usecases in an enterprise. Characterized by their speed in realizing business value and support for diverse use cases, Sub-ML applications still require guarantees of correctness, transparency, and auditability in the data transformation process. We draw on our experience in the fin-tech, ed-tech and e-commerce domains to lay out design choices for feature stores to enable Sub-ML, tradeoffs we made including constraining the problem space, bundling capabilities for fast development, and incorporating a data consumption layer.

Slides

https://www.slideshare.net/pingali/fast-subml-usecase-developmentpdf