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Piero Molino was one of the founding members of Uber AI Labs. He worked on several deployed ML systems, including an NLP model for Customer Support, and the Uber Eats Recommender System. He is the author of Ludwig , an open source declarative deep learning framework. In 2021 he co-founded Predibase, the low-code declarative machine learning platform built on top of Ludwig.Piero's LinkedIn: https://www.linkedin.com/in/pieromolino
Predibase free access: bit.ly/3PCeqqw
Daliana's Twitter: https://twitter.com/DalianaLiu
Daliana's LinkedIn: https://www.linkedin.com/in/dalianaliu
(00:00:00) Introduction
(00:01:54) Journey to machine learning
(00:03:51) Recommending system at Uber Eats
(00:04:13) Projects at Uber AI
(00:09:34) Uber's customer obsession ticket system
(00:16:01) How to evaluate online-offline business and model performance metrics
(00:17:16) Customer Satisfaction
(00:28:38) When do you know whether a project is good enough
(00:41:50) Declarative machine learning and Ludwig
(00:45:32) Ludwig vs AutoML
(00:54:44) Working with Professor Chris Re
(00:58:32) Why he started Predibase
(01:07:56) LLM and GenAI
(01:10:17) Challenges for LLMs
(01:22:36) Advice for data scientists
(01:34:29) Career advice to his younger self
By Daliana Liu4.7
7575 ratings
Piero Molino was one of the founding members of Uber AI Labs. He worked on several deployed ML systems, including an NLP model for Customer Support, and the Uber Eats Recommender System. He is the author of Ludwig , an open source declarative deep learning framework. In 2021 he co-founded Predibase, the low-code declarative machine learning platform built on top of Ludwig.Piero's LinkedIn: https://www.linkedin.com/in/pieromolino
Predibase free access: bit.ly/3PCeqqw
Daliana's Twitter: https://twitter.com/DalianaLiu
Daliana's LinkedIn: https://www.linkedin.com/in/dalianaliu
(00:00:00) Introduction
(00:01:54) Journey to machine learning
(00:03:51) Recommending system at Uber Eats
(00:04:13) Projects at Uber AI
(00:09:34) Uber's customer obsession ticket system
(00:16:01) How to evaluate online-offline business and model performance metrics
(00:17:16) Customer Satisfaction
(00:28:38) When do you know whether a project is good enough
(00:41:50) Declarative machine learning and Ludwig
(00:45:32) Ludwig vs AutoML
(00:54:44) Working with Professor Chris Re
(00:58:32) Why he started Predibase
(01:07:56) LLM and GenAI
(01:10:17) Challenges for LLMs
(01:22:36) Advice for data scientists
(01:34:29) Career advice to his younger self

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