Anecdotally, only 2% of the models developed are productionized, i.e., used day to day to improve business outcomes. Part of the reason is the high cost and complexity of productionization of models. It is estimated to be anywhere from 40 to 80% of the overall work. In this talk, we will share Scribble Data's insights into productionization of ML, and how to reduce the cost and complexity in organizations. It is based on the last two years of work at Scribble developing and deploying production ML Feature Engineering Platform, and study of platforms from major organizations such as Uber. This talk expands on a previous talk given in January. First, we discuss the complexity of production ML systems, and where time and effort goes. Second, we give an overview of feature engineering, which is an expensive ML task, and the associated challenges Third, we suggest an architecture for Production Feature Engineering platform. Last, we discuss how one could go about building one for your organization More details: https://confengine.com/odsc-india-2019/proposal/9675 Conference Link: https://india.odsc.com

This talk addresses the following questions: 1. What should a production ML feature engineering platform have and why? 2. When do I need one? 3. What are my options if I have to build one? This talk draws upon the Scribble’s experience in building and evolving a production feature engineering platform, and the many conversations we have had with user data scientists. The talk will focus on the learnings, and not on the Scribble product itself, and expand on the talk from Fifth Elephant Mumbai in Jan 2019 on reducing costs. Dr. Venkata Pingali is Co-Founder and CEO of Scribble Data, an ML Engineering company based in Bangalore and Denver. Scribble’s flagship enterprise product, Enrich, accelerates ML productionization in enterprises. Before starting Scribble Data, Dr. Pingali was VP of Analytics at a political data consulting firm. He has a BTech from IIT Mumbai and a PhD from USC in Computer Science.

MLOps Community Meetup #6 Mid-Scale Production Feature Engineering In the 4th online meetup for our MLOps.community We spoke with Dr. Venkata Pingali, CEO of Scribble Data and Feature engineering expert! Scribble Data 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 learned 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. 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 use cases 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. This was a virtual fireside chat between Dr. Venkata Pingali, Demetrios Brinkmann and the MLOps community. Relevant links can be found below. Join our MLOps slack community: https://bit.ly/3aOTwgR Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Dr. Venkata Pingali on Linkedin: https://www.linkedin.com/in/pingali/