Seamlessly Scaling AI for Distributed Big Data

Originally published at LinkedIn Pulse. Early last month, I presented a half-day tutorial on at this year's virtual CVPR 2020. This is a very unique experience, and I would like to share some of the highlights of the tutorial. The tutorial focused on a critical problem that arises as AI moves from experimentation to production; that is, how to seamlessly scale AI to distributed Big Data. Today, AI researchers and data scientists need to go through a mountain of pains to apply AI models to production dataset that is stored in distributed Big Data cluster.