Looking for more episodes? Check all of our episodes right here below!
AutoGluon: The Story
Today we're talking with Nick Erickson from AutoGluon. We discuss AutoGluon's fascinating origin story, its unique point of view, the science and engineering behind some of its unique contributions, Amazon's Machine Learning University, AutoGluon's multi-layer stack ensembler in...
How to Integrate Logic and Argumentation into Human-Centric AutoML
Today we're talking with Joseph Giovanelli about his work on integrating logic and argumentation into AutoML systems. Joseph is a PhD student at the University of Bologna. He was more recently in Hannover working on ethics and fairness with Marius’ team. The paper he published presents his fra...
How to Design an AutoML System using Error Decomposition
Today we're talking with Caitlin Owen, a post-doc at the University of Otago about her work on error decomposition. She recently published a paper titled "Towards Explainable AutoML Using Error Decomposition" about how a more granular view of the components of error can lead the cons...
The Semantic Layer and AutoML
Today we're talking with Gaurav Rao, the EVP & GM of Machine Learning and AI at AtScale, a company centered around the semantic layer. For some time now, I've been feeling that there is a deep connection between a formal articulation of business context and the realization of the drea...
Foundation Models: The term and its origins
Today Ankush Garg is speaking with Rishi Bommasani, PhD student at Stanford and one of the originator of the term Foundation Models. They’re talking about the origins of the term Foundation Model, which he and his group advanced, in the paper "On the Opportunities and Risks of Foundation Model...
The Business and Engineering of AutoML Products with Raymond Peck
Today we're talking with Raymond Peck, a senior engineer and director in the AutoML space. He spent time at H2O, dotData, Alteryx and many other places. This is a fascinating conversation about the business, engineering, and science of machine learning automation in production. Learning about ...