This podcast helps Machine Learning Engineers become the best at what they do. Join host Charlie You every week as he talks to the brightest minds in data science, artificial intelligence, and software engineering to discover how they bring cutting edge research out of the lab and into products that people love. You'll learn the skills, tools, and best practices you can use to build better ML systems and accelerate your career in this flourishing new field.
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This podcast helps Machine Learning Engineers become the best at what they do. Join host Charlie You every week as he talks to the brightest minds in data science, artificial intelligence, and software engineering to discover how they bring cutting edge research out of the lab and into products that people love. You'll learn the skills, tools, and best practices you can use to build better ML systems and accelerate your career in this flourishing new field.
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.
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.
Pavle talks about his vision for a post-scarcity future, and how his company, Aether Bio, is using machine learning to accelerate towards that by creating and modeling enzymatic reactions.
Andreas discusses the state of ML research for music information retrieval, the future of tools for data science and ML engineering, and Replicate, his recent project aiming to solve version control for ML models.
Luigi discusses best practices for putting ML into production, how to make sure your data science efforts are actually adding business value, and what the future of building software might be ("Code 2.0").
swyx returns to the podcast to discuss why and how you should negotiate your salary, getting started in public speaking, creating your own luck, learning in public, and much, much more!
Yannic talks about starting his popular paper-explainer Youtube channel, how he reads for understanding, why the peer review process is broken, and where he thinks the AI field is going.
Letitia discusses multi-modal machine learning, the sub-field studying models that integrate multiple kinds of information (vision, language, etc.). She also talks about the need for effective communication of AI topics to the general public and her attempt to do so in the form of the excellent YouTube channel AI Coffee Break.
Moin discusses his research in NLP, how language models can learn to reason with knowledge graphs, and what the future of the field looks like given recent advancements in AI hardware.
This podcast helps Machine Learning Engineers become the best at what they do. Join host Charlie You every week as he talks to the brightest minds in data science, artificial intelligence, and software engineering to discover how they bring cutting edge research out of the lab and into products that people love. You'll learn the skills, tools, and best practices you can use to build better ML systems and accelerate your career in this flourishing new field.
