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Demetrios Brinkmann and Vishnu Rachakonda on how they built the world's largest online hub for production Machine Learning practitioners
Publisher |
Prateek Joshi
Media Type |
audio
Categories Via RSS |
Technology
Publication Date |
May 16, 2022
Episode Duration |
00:41:35

Demetrios Brinkmann is one of the main organizers of the MLOps Community and currently resides in a small town outside Frankfurt, Germany. He is an avid traveler who taught English as a second language to see the world and learn about new cultures. He fell into the Machine Learning Operations world, and since, has interviewed the leading names around MLOps, Data Science, and Machine Learning. Since diving into the nitty-gritty of ML Operations he felt a strong calling to explore the ethical issues surrounding AI/ML. When he is not conducting interviews, you can find him making stone stackings with his daughter in the woods or playing the ukulele by the campfire.Vishnu Rachakonda is a data scientist at Firsthand, where he helps build data-intensive systems that identify and connect individuals living with serious mental illness to firsthand’s peer-based recovery model. Vishnu is also the Head of Operations for the MLOps Community, the world’s largest online hub for production ML practitioners, and co-hosts the community’s podcast "MLOps Coffee Sessions", whose past guests include Jeremy Howard, D. Sculley, and other industry luminaries. Prior to this, he was the first machine learning hire at Tesseract Health, a 4Catalyzer company focused on ophthalmic imaging, and a teaching assistant for the spring 2021 edition of Full Stack Deep Learning. He obtained a BS and MS in bioengineering from the University of Pennsylvania.In this episode, we cover a range of topics including:- Journey into machine learning- How they started the MLOps community and grew it into the world's largest community of its kind- Framework for building online communities and getting the first few members to join- What tools can be used to manage online communities- ML tooling landscape- What signals can we use to identify ML tools that are gaining traction before it's obvious- Why should an ML tool be opinionated

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