You don't need a bigger boat: MLOps on a reasonable scale
Speaker: Jacopo Tagliabue

Abstract:
It is indeed a wonderful time to be building machine learning systems because we don't have much to do anymore! Thanks to a growing ecosystem of tools and shared best practices, even small teams at 'reasonable scale' can be incredibly productive. In this talk we present our philosophy for modern, no-nonsense data pipelines, highlighting the benefits of a PaaS approach and demonstrating (with open source code) how the entire toolchain works on real-world data with realistic constraints. We conclude by discussing our proposal for self-documenting ML DAGs – 'DAG cards' for Metaflow – and sharing unsolicited advice on the future of MLOps for 'reasonable' companies.

Bio:
Jacopo Tagliabue is trained in various acronyms around the world (UNISR, SFI, MIT) and was co-founder and CTO of Tooso, a San Francisco AI company acquired by Coveo in 2019. Jacopo is currently the Lead AI Scientist at Coveo, shipping models for hundreds of customers and millions of users. When he's not building products, he explores topics at the intersection of language, reasoning, and learning: his research and industrial work is often featured in the general press and at leading AI venues. In past lives, he managed to get a PhD, do scientific stuff for a professional basketball team, and simulate a pre-Columbian civilization.

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0:00 Starts soon
2:06 Presentation
35:02 Discussion

The Stanford MLSys Seminar is hosted by Dan Fu, Karan Goel, Fiodar Kazhamiaka and Piero Molino, Chris Ré and Matei Zaharia.

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#machinelearning #ai #artificialintelligence #systems #mlsys #computerscience #stanford #coveo #tooso #aitools

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