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RecSys at Reasonable Scale, Data Science in Ag, and Setting Sail with GitOps

Posted Sep 23, 2022 | Views 676
# Recommender Systems
# Reasonable Scale
# Data Science
# GitOps
# cargill.com
# interos.ai
# coveo.com
# flatiron.com
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speakers
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Jacopo Tagliabue
Director of AI @ Coveo

Educated in several acronyms across the globe (UNISR, SFI, MIT), Jacopo Tagliabue was co-founder and CTO of Tooso, an A.I. company in San Francisco acquired by Coveo in 2019. Jacopo is currently the Director of AI at Coveo, shipping models to hundreds of customers and millions of users. When not busy building products, he is exploring topics at the intersection of language, reasoning, and learning: his research and industry work is often featured in the general press and premier A.I. venues. In previous lives, he managed to get a Ph.D., do sciency things for a pro basketball team, and simulate a pre-Columbian civilization.

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Felipe Campos Penha
Senior Data Scientist @ Cargill

Felipe Penha creates content about Data Science regularly on the Data Science Bits channel on YouTube and Twitch. He has 8+ years of experience with hands-on data-related work, starting with his doctorate in Astroparticle Physics. His career in the private sector has been devoted to bringing value to various segments of the Food and Beverages Industry through the use of Analytics and Machine Learning.

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Sam Wilkinson
Data Science Manager @ Flatiron Health

Currently building models for use in cancer research at Flatiron Health. Previously Data Science/ML at NASA, European Space Agency, Petal, and Nested. Oxford Physics graduate.

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Stephan Brown
MLOps Engineer @ Interos Inc.

Stephan specializes in Natural Language Processing and deep learning. He has been working full-time doing data science and machine learning for 4 years. Stephan also has experience in many other domains such as game, web, VR/AR, and mobile development.

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SUMMARY

In this talk, the speakers discuss collaboration with the Outerbounds, NVIDIA Merlin, and Comet teams to release as open source code a realistic data and ML pipeline for cutting-edge recommender systems “that just works”. Anyone can cook great ML, not just Big Tech if you know how to pick and choose your tools.

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