Speaker "Anand Sampat" Details Back



Provenance in production-grade machine learning — a guide to effective AI deployment


Over the next few years, every company must develop a strategy to leverage artificial intelligence and machine learning to stay relevant and beat out competitors. This requires hiring talented data scientists as well as DevOps and data engineers who can put these into production. Today, finding that perfect combination of talent can be difficult, but a focus on retraining and productivity tools can increase a small team’s impact on business ROI by over 10x. In this technical talk, we discuss how enterprises can better prepare their employees to deploy artificial intelligence and machine learning into production by using the same techniques used in software to add provenance, reliability, and efficiency to these processes. Specifically, we describe the benefits of adding provenance including reliable deployments and builds, A/B testing, continuous deployment, and automation and show how they can decrease the time to business ROI by over 10x.


Anand has been deeply involved in data science for the last 10 years. As a researcher and student at UC Berkeley, he brought machine learning to traditional science. He then built recommendation systems at Collegefeed (acquired by After College). Most recently at Stanford, he led the Data Science Club, and taught the school-wide machine learning course, CS 229 while he was a graduate student. Anand is enthusiastic about empowering all individuals across industries to realize the potential of data science. He holds an MS from Stanford University and and a BS in Electrical Engineering and Computer Science from UC Berkeley. Apart from data, he loves music, dance, and marathon running.