AI Portfolio Podcast
The AI Portfolio Podcast showcases Experts, Companies, and Communities that can accelerate your journey of taking machine learning products to market.
If you are a practitioner, investor, or data leader, you will get something from the show by becoming exposed to great companies to invest in or join and learn how experts navigate their careers.
My goal is to open doors and increase your sense of the possibility of what can be done with machine learning. Connect with me, share the show, and let me know how I can add value.
AI Portfolio Podcast
Jacopo Tagliabue: Data Workloads, Recommender Systems, Startups and LLM Developments - AI Portfolio
Jacopo Tagliabue is one of the most honest and talented open-source contributors in the Data Science community, previously focusing on NLP and Recommender Systems. Currently, he eats pizza, plays very occasional tennis, and is building a data pipeline company, Bauplan.
A true founder and builder at heart, Jacapo and team pioneered the movement for doing data science at reasonable scale. His NLP company Tooso, was acquired by Coveo where he led Data Science for search all the way to an IPO.
He
- has been educated at top institutions such as UNISR, SFI, and MIT
- has collaborated on open-source research with Stanford, Netflix, Farfetch, and NVIDIA
- has invested in startups and funds as an LP
- teaches machine learning at NYU as an adjunct professor
📲 Jacopo Tagliabue Socials:
LinkedIn: https://www.linkedin.com/in/jacopotagliabue/ (don't try to sell him anything)
Twitter: https://twitter.com/jacopotagliabue
Website: https://jacopotagliabue.it/
📲 Mark Moyou, PhD Socials:
LinkedIn: https://www.linkedin.com/in/markmoyou/
Twitter: https://twitter.com/MarkMoyou
📗 Chapters
00:00 Intro
03:34 LLM Progress
09:07 Small LLMs
11:38 Search and Information Retrieval
15:12 Recommender System
19:20 Robust Recommender System
22:37 Hardest Domain for Recommender System
24:54 Fake Datasets for Research
26:48 Mistakes in Building Recommender System
30:10 Building a Reasonable Recommender System
33:46 Reasonable Scale
38:17 What Bauplan is trying to accomplish?
44:41 Data Processing
50:05 Build Vs Buy
55:12 Building a New Startup
57:35 Tennis
58:31 Investments in ML Community
01:02:14 First Company
01:10:37 Advice for Founders/Lessons Learned from First Startup
01:15:03 PhD
01:18:39 Career Optimization Function
01:23:06 Advice for Open Source Contribution (Do the freaking commit!)
01:27:30 Journal for Failed Papers
01:29:53 Book Recommendations
01:34:00 Advice for High Schooler, College Student & Professional
01:36:10 Rapid Round