Machine Learning Engineered

Alex discusses his background working at the NSA and then starting Harvest.ai, which was acquired by and integrated into AWS. He then goes into his latest venture, Gretel.ai, which provides tools for creating anonymized and synthetic datasets.

Radek details his journey switching careers into software engineering and then into machine learning. He talks about mistakes he made, how he would do it now, and gives a preview of his forthcoming book.

Rodrigo details his journey from selling a company to leading data science teams at top companies to researching machine learning. He also touches on his research interests in time series data and topological data analysis.

Dan discusses why he's so excited about the future of machine learning, where it is on the technology adoption curve, the rise of a "canonical stack" of AI infrastructure, and practically approaching the hard problems in AI ethics.

Willem discusses his experience building the ML platform at Gojek and what he's learned from developing and open-sourcing Feast. He also goes into his vision for it's future and how teams can best get started adopting it.

Benedikt discusses common problems faced by teams putting machine learning into production, bringing over best practices from DevOps to solve them, and building ZenML, an open source MLOps framework.

Josh talks about pivoting from an AI recruiting startup to Generally Intelligent, an independent AI research lab. He also touches on how he defines general intelligence, what his lab is working on now, and how he creates the optimal research environment.

Elena and Emeli of Evidently AI discuss what they've learned applying ML across a wide variety of industries, including manufacturing and industrial process improvement, and then go into why they've started building tools for data and ML monitoring as well as how teams can do it better.

Harikrishna discusses what it means to be an effective data science manager and how he thinks about hiring for machine learning roles

I was recently interviewed by Demetrios and Vishnu from the ML Ops Community podcast. We discuss my experience working as an ML engineer and starting the podcast, lessons learned from talking to experts, and trends we've noticed in the industry.