Wikimedia MLOps
In this episode
Chris Albon
Director of Machine Learning, Wikimedia Foundation
Chris spent over a decade applying statistical learning, artificial intelligence, and software engineering to political, social, and humanitarian efforts. He is the Director of Machine Learning at the Wikimedia Foundation. Previously, Chris was the Director of Data Science at Devoted Health, Director of Data Science at the Kenyan startup BRCK, cofounded the AI startup Yonder, created the data science podcast Partially Derivative, was the Director of Data Science at the humanitarian non-profit Ushahidi and was the director of the low-resource technology governance project at FrontlineSMS. Chris also wrote Machine Learning For Python Cookbook (O’Reilly 2018) and created Machine Learning Flashcards. Chris earned a Ph.D. in Political Science from the University of California, Davis researching the quantitative impact of civil wars on health care systems. He earned a B.A. from the University of Miami, where he triple majored in political science, international studies, and religious studies.
Demetrios Brinkmann
Host
Demetrios is one of the main organizers of the MLOps community and currently resides in a small town outside Frankfurt, Germany. He is an avid traveller who taught English as a second language to see the world and learn about new cultures. Demetrios fell into the Machine Learning Operations world, and since, has interviewed the leading names around MLOps, Data Science, and ML. Since diving into the nitty-gritty of Machine Learning Operations he felt a strong calling to explore the ethical issues surrounding ML. When he is not conducting interviews you can find him making stone stacking with his daughter in the woods or playing the ukulele by the campfire.
Neal Lathia
Host
👋 Neal is currently the Machine Learning Director at Monzo in London, where they’re focusing on building machine learning systems that optimize the app and help the company scale. ✈️ Before joining Monzo, Neal was a Data Scientist at Skyscanner, where he built recommender and ranking systems to improve travel information in the app. Neal's work has always focused on applications that use machine learning - this has taken him from recommender systems to urban computing and travel information systems, digital health monitoring, smartphone sensors, and banking. You can read more about his work and research in the Press & Speaking and Research sections.