Cloud Directions, MLOps and Production Data Science


Cloud computing promises to simplify infrastructure, but somehow MLOps remains deeply technical, even in the cloud. The complexity of MLOps tends to lead to an organizational antipattern: data scientists who know the data and models best have to mind-meld with data engineers who know the infrastructure best. This is particularly problematic in the highest-value stage of the ML lifecycle — managing models in production.

Recent trends in cloud technology, including serverless computing, promise new approaches for abstracting away infrastructure. Unfortunately these offerings fall short of the challenge of MLOps. In this talk I will cover some of the important promises and weaknesses of current cloud offerings, and describe research from Berkeley's RISElab and the resulting open source Aqueduct system, which are putting Production Data Science at the fingertips of anyone working with data and models.


Joseph M. Hellerstein is the Jim Gray Professor of Computer Science at the University of California, Berkeley, whose work focuses on data-centric systems and the way they drive computing. He is an ACM Fellow, an Alfred P. Sloan Research Fellow and the recipient of three ACM-SIGMOD ""Test of Time"" awards for his research. Fortune Magazine has included him in their list of 50 smartest people in technology , and MIT's Technology Review magazine included his work on their TR10 list of the 10 technologies ""most likely to change our world"".

Hellerstein is a co-founder of Aqueduct, which is bringing new open source technology for Prediction Infrastructure to market. Previously he co-founded Trifacta, the pioneering company in Data Preparation, where he served as founding CEO and Chief Strategy Officer. Hellerstein has served on the technical advisory boards of a number of computing and Internet companies including Dell EMC, SurveyMonkey, Datometry and Acryl Data.

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