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October 11-15
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Friday, October 15 • 5:25pm - 6:00pm
Scaling Kubeflow for Multi-tenancy at Spotify - Keshi Dai & Jonathan Jin, Spotify

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Spotify began offering a centralized Kubeflow Pipelines product to its machine learning teams around two years ago. Since then, adoption has skyrocketed, with more teams training more models and running increasingly complex experiments. These increased demands on our system come with more stringent demands on us, the Kubeflow team at Spotify, to ensure not just cluster reliability, but cluster equitability. Our job is to not just be cluster maintainers, but cluster stewards—ensuring equitable and reliable access to cluster resources, and keeping users from stepping on each others’ toes. In this talk, we’ll discuss our streamlined tooling to maintain, deploy, and monitor Spotify’s distribution of Kubeflow. We’ll illustrate the challenges we face as we scale to increased user load and increasingly distinct and demanding pipelines, and outline our approach to addressing those challenges with “multi-cluster” Kubeflow. Finally, we’ll give a preview of our future plans for the platform.

Speakers
avatar for Keshi Dai

Keshi Dai

Senior ML Infrastructure Engineer, Spotify
Keshi Dai is a Senior Engineer at Spotify who works on Machine Learning platform. His team is building a centralized Kubeflow platform to help Machine Learning engineers at Spotify to adopt Kubernetes. Previously, Keshi also worked on Discover Weekly and Release Radar at Spotify... Read More →
avatar for Jonathan Jin

Jonathan Jin

Senior ML Infrastructure Engineer, Spotify
Jonathan Jin is a senior engineer at Spotify working on machine learning platform and infrastructure. Previously, he has worked on AI infrastructure for NVIDIA and Twitter. He has also worked on observability infrastructure at Uber.


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Friday October 15, 2021 5:25pm - 6:00pm PDT
411 Theater + Online