The New DBfication of ML/AI
by Arun Kumar

Abstract:
The recent boom in ML/AI applications has brought into sharp focus the pressing need for tackling the concerns of scalability, usability, and manageability across the entire lifecycle of ML/AI applications. The ML/AI world has long studied the concerns of accuracy, automation, etc. from theoretical and algorithmic vantage points. But to truly democratize ML/AI, the vantage point of building and deploying practical systems is equally critical.

In this talk, Professor Kumar will make the case that it is high time to bridge the gap between the ML/AI world and a world that exemplifies successful democratization of data technology: databases. He will show how new bridges rooted in the principles, techniques, and tools of the database world are helping tackle the above pressing concerns and in turn, posing new research questions to the world of ML/AI. As case studies of such bridges, he will describe two lines of work from his group: query optimization for ML systems and benchmarking data preparation in AutoML platforms. He will conclude with his thoughts on community mechanisms to foster more such bridges between research worlds and between research and practice.

Bio:
Arun Kumar is an Assistant Professor in the Department of Computer Science and Engineering and the Halicioglu Data Science Institute and an HDSI Faculty Fellow at the University of California, San Diego. He is a member of the Database Lab and Center for Networked Systems and an affiliate member of the AI Group. His primary research interests are in data management and systems for machine learning/artificial intelligence-based data analytics. Systems and ideas based on his research have been released as part of the Apache MADlib open-source library, shipped as part of products from Cloudera, IBM, Oracle, and Pivotal, and used internally by Facebook, Google, LogicBlox, Microsoft, and other companies. He is a recipient of three SIGMOD research paper awards, four distinguished reviewer/metareviewer awards from SIGMOD/VLDB, the IEEE TCDE Rising Star Award, an NSF CAREER Award, a UCSD oSTEM Faculty of the Year Award, and research award gifts from Amazon, Google, Oracle, and VMware.

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Agenda (Pacific Daylight Time, UTC -07)

• 5:30 - 5:40 pm -- Gathering and introductions
• 5:40 - 6:30 pm -- Talk
• 6:30 - 7:00 pm -- Q & A, discussion

Links to slides and videos of meetup presentations are available on the SDML GitHub repo https://github.com/SanDiegoMachineLearning/talks

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