Wikimedia MLOps // Chris Albon // Coffee Sessions #68
MLOps.community
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MLOps Coffee Sessions #68 with Chris Albon, Wikimedia MLOps co-hosted by Neal Lathia.
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// Abstract
Chris Alban (Wikimedia ML team lead) and Neil Lithia discuss Alban's high-output drive, Wikimedia's open-source ML infrastructure, and a six-person team's role in maintaining editor-assist models like article quality prediction and mobile "Add-a-Link" features. Key topics include agile workflows for rapid model deployment via Kubeflow, repeatability through enforced policies, open-source tooling challenges (e.g., AMD GPUs), ethical governance with model cards, and community-trained models. The session highlights Wikimedia's lean, donation-funded scale and calls for contributions.
// Bio
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.
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Connect with Chris on LinkedIn: https://www.linkedin.com/in/chrisralbon/
Timestamps:
[00:00] Introduction to Chris Albon
[00:28] Do you sleep? :-)
[02:43] ML at Wikimedia
[09:27] Wikimedia workflow
[15:00] Creating a repeatable process
[19:11] Wikimedia element team size
[20:47] Wikimedia workflow and hardware
[23:56] Evaluating open source
[29:20] Lacking in ML source tooling
[33:11] Wikimedia's separate data platform
[38:14] Abstractions
[41:50] Experimentation aspect of getting models into production
[44:05] Stack of Abstraction in ML
[47:16] Chris' proudest model
[49:10] How Wikimedia works with communities
[55:24] Large language models
[1:02:16] Beautiful vision
[1:03:23] Wrap up
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