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apply(recsys) Conference 2022 | Lessons Learned: The Journey to Operationalizing Recommender Systems

Posted Dec 12, 2022 | Views 310
# apply(recsys) 2022
# Panel Discussion
# Production Use Case
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SPEAKERS
Mike Del Balso
Mike Del Balso
Mike Del Balso
Co-Founder & CEO @ Tecton

Mike Del Balso is the co-founder of Tecton, where he is focused on building next-generation data infrastructure for Operational ML. Before Tecton, Mike was the PM lead for the Uber Michelangelo ML platform. He was also a product manager at Google where he managed the core ML systems that power GoogleÕs Search Ads business. Previous to that, he worked on Google Maps. He holds a BSc in Electrical and Computer Engineering summa cum laude from the University of Toronto.

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Mike Del Balso is the co-founder of Tecton, where he is focused on building next-generation data infrastructure for Operational ML. Before Tecton, Mike was the PM lead for the Uber Michelangelo ML platform. He was also a product manager at Google where he managed the core ML systems that power GoogleÕs Search Ads business. Previous to that, he worked on Google Maps. He holds a BSc in Electrical and Computer Engineering summa cum laude from the University of Toronto.

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Jacopo Tagliabue
Jacopo Tagliabue
Jacopo Tagliabue
MLSys Professor @ NYU

Educated in several acronyms across the globe (UNISR, SFI, MIT), Jacopo Tagliabue was co-founder of Tooso, an A.I. company acquired by Coveo in 2019. Jacopo is currently the Director of A.I. at Coveo, shipping models to hundreds of customers and millions of users. When not busy building products, he teaches MLSys at NYU and explores topics at the intersection of language, reasoning and learning (with research work presented at NAACL, RecSys, ACL, SIGIR). 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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Educated in several acronyms across the globe (UNISR, SFI, MIT), Jacopo Tagliabue was co-founder of Tooso, an A.I. company acquired by Coveo in 2019. Jacopo is currently the Director of A.I. at Coveo, shipping models to hundreds of customers and millions of users. When not busy building products, he teaches MLSys at NYU and explores topics at the intersection of language, reasoning and learning (with research work presented at NAACL, RecSys, ACL, SIGIR). 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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Marc Lindner
Marc Lindner
Marc Lindner
Co-founder and CPO @ eezylife

Background is in Knowledge Engineering.

Built eezy with the vision that recommender systems should not be owned by advertisers but by users. Integrating companies such as spotify, ticketmaster, netflix, opentable and many more.

Helped a construction engineering firm build a 3D modeling technique using photogrammetry with CNN's detecting faults and placing them on the 3D model.

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Background is in Knowledge Engineering.

Built eezy with the vision that recommender systems should not be owned by advertisers but by users. Integrating companies such as spotify, ticketmaster, netflix, opentable and many more.

Helped a construction engineering firm build a 3D modeling technique using photogrammetry with CNN's detecting faults and placing them on the 3D model.

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Agnes van Belle
Agnes van Belle
Agnes van Belle
Team Lead Data Science @ HeyJobs

Agnes van Belle works as a Data Science / Machine Learning Manager at HeyJobs in Berlin, focusing on algorithmic marketing (campaign budget optimization) as well as job board recommendations and search, all with the goal of matching the most qualified blue-collar jobseekers to their most desired jobs.

Before, she worked as a Data Scientist at OLX Group in Berlin for the Search and Experimentation teams, optimizing users' experience to successfully buy and sell via online classifieds by e.g. deal closure prediction, smart search expansions, and various A/B-testing improvements. Further back she worked as Search R&D team lead at Textkernel, an Amsterdam-based company that enables the HR industry to automatically match people with jobs by parsing and ranking CVs and vacancies.

Agnes got her M.Sc. in Artificial Intelligence at the University of Amsterdam, and has spoken or been a panel member at Berlin Buzzwords, ECIR, ECML-PKDD, Haystack Europe, Women Techmakers Berlin and DataTalks.Club.

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Agnes van Belle works as a Data Science / Machine Learning Manager at HeyJobs in Berlin, focusing on algorithmic marketing (campaign budget optimization) as well as job board recommendations and search, all with the goal of matching the most qualified blue-collar jobseekers to their most desired jobs.

Before, she worked as a Data Scientist at OLX Group in Berlin for the Search and Experimentation teams, optimizing users' experience to successfully buy and sell via online classifieds by e.g. deal closure prediction, smart search expansions, and various A/B-testing improvements. Further back she worked as Search R&D team lead at Textkernel, an Amsterdam-based company that enables the HR industry to automatically match people with jobs by parsing and ranking CVs and vacancies.

Agnes got her M.Sc. in Artificial Intelligence at the University of Amsterdam, and has spoken or been a panel member at Berlin Buzzwords, ECIR, ECML-PKDD, Haystack Europe, Women Techmakers Berlin and DataTalks.Club.

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Krystal Zeng
Krystal Zeng
Krystal Zeng
ML Engineer @ Cookpad

I am Krystal Zeng, currently a Machine Learning Engineer at Cookpad working on the recipe recommendation engine. I have worked as a Data Scientist and Software Engineer in the past.

These days, I'm all about RecSys and MLOps!

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I am Krystal Zeng, currently a Machine Learning Engineer at Cookpad working on the recipe recommendation engine. I have worked as a Data Scientist and Software Engineer in the past.

These days, I'm all about RecSys and MLOps!

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SUMMARY

Join us in this panel discussion to hear from ML practitioners on their journey to implementing Recommender Systems. We’ll discuss the most common challenges encountered when getting started, and best practices to address them. We’ll explore organizational dynamics, recommended tools, and how to align business requirements with technical capabilities. You’ll hear about approaches to phasing in Recommender Systems, starting small and progressively iterating on more sophisticated solutions.

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