Scribble helps build and operate production feature engineering platforms for sub-fortune 1000 firms. The output of the platforms is consumed by data science and analytical teams. In this talk we discuss how we understand the problem space, and the architecture of the platform that we built for preparing trusted model-ready datasets that are reproducible, auditable, and quality checked, and the lessons learnt in the process. We will touch upon topics like classes of consumers, disciplined data transformation code, metadata and lineage, state management, and namespaces. This system and discussion complements work done on data science platforms such as Domino and Dotscience.
In this episode
Co-Founder and CEO, Scribble Data
Dr. Venkata Pingali is Co-Founder and CEO of Scribble Data, an ML Engineering company with offices in India and Canada. Scribble’s flagship enterprise product, Enrich, enables organizations to address 10x analytics/data science usecases through trusted production datasets . Before starting Scribble Data, Dr. Pingali was VP of Analytics at a data consulting firm and CEO of an energy analytics firm. He has a BTech from IIT Mumbai and a PhD from USC in Computer Science.
Demetrios is one of the main organizers of the MLOps community and currently resides in a small town outside Frankfurt, Germany. He is an avid traveller who taught English as a second language to see the world and learn about new cultures. Demetrios fell into the Machine Learning Operations world, and since, has interviewed the leading names around MLOps, Data Science, and ML. Since diving into the nitty-gritty of Machine Learning Operations he felt a strong calling to explore the ethical issues surrounding ML. When he is not conducting interviews you can find him making stone stacking with his daughter in the woods or playing the ukulele by the campfire.