Episode description

Jacopo Tagliabue is currently the founder of Bauplan and an adjunct professor of ML Systems at NYU. He was also the co-founder and CTO of Tooso, a NLP / IR startup in San Francisco acquired by Coveo. He led Coveo’s AI and MLOps roadmap from scale-up to IPO, and built out Coveo Labs, an applied R&D practice rooted in collaboration, open source, and open science.

Jacopo recently joined us at Arthur HQ for a chat with our Chief Scientist John Dickerson about:

• His experience from founder to acquisition through IPO
• What it’s like integrating an MLOps team into a larger company
• His new company, Bauplan
• Predictions for the future of natural language, and what’s overhyped in the space
• How recommender systems will impact the workforce, politics, and elections

Watch the video, including Jacopo’s presentation prior to the Q&A, on YouTube: https://youtu.be/EAjoTnyVGFs  

Learn more about Arthur: https://www.arthur.ai 

Learn more about Jacopo: https://jacopotagliabue.it/  

—

[1:36] Tell us a bit about yourself, your path to tech, NLP, Tooso, what it was like going through an IPO, etc.

[3:21] What is it like integrating an MLOps team into a larger company? And how does MLOps scale during that scale-up phase?

[5:21] You’ve done a lot of publicity around talking about enabling applied research at a reasonable scale or at a startup scale. Not at Microsoft or Google scale—at a strong company, but not one with the deepest pockets in the world. Would you like to share any learnings from that?

[9:37] Do you have suggestions for somebody going from zero to one building out their own MLOps stack right now?

[12:08] Were there any learnings or feedback from the community regarding RecList that you’d like to share or that impacted future versions?

[13:35] I know you’re founding a new company. Want to share a quick sentence or two on that?

[14:41] What’s hot or what’s overhyped right now in natural language? Any short- or long-term predictions?

[18:27] Audience Q&A

[18:36] How do you think about the causal effects of multiple recommendations, one after another, like a banded-style evaluation? Do you think that’s the kind of thing that only big tech companies need to care about, or is the kind of thing that smaller companies should think about in the evaluation of the recommendation systems?

[20:04] What’s the most exciting opportunity you see in recommender systems in the coming years?

[21:23] You mentioned you had a contentious opinion about using ChatGPT to do model deployment. Can you expand on that? 

[22:17] What do you think about offline policy evaluation?

[24:20] I know we’re talking a lot about how language models will change the workforce at large and put people out of their jobs and things like that. How do you think that advances in recommendations will impact larger career changes and workforce things? 

[26:05] What about recommending in the political space and how that is impacting elections, democracy, things like that?