Feature Store Summit - The SAME Project: A Cloud Native Approach to Reproducible Machine Learning

Wednesday, October 13, 12:55pm - 1:25pm (EDT)

Time shown in-04:00 America, New York

This presentation is part of the Feature Store Summit
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We live in a time of both feast and famine in machine learning. Large organizations are publishing state-of-the-art models at an ever-increasing rate but most data scientists still face daunting challenges to reproduce the results themselves. Even in the best cases, where a newly forked code runs without syntax errors (often not the case), this only solves a part of the problem as the pipelines used to run the models are often completely excluded.

The Self-Assembling Machine Learning Environment (SAME) project is a new Kubernetes and Kubeflow project and community around a common goal: creating tooling that allows for quick ramp-up, seamless collaboration and efficient scaling. This talk will discuss our initial public release, done in collaboration with data scientists from across the spectrum, where we are going next and how people can use our learnings in their own practices.

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Add to Calendar 2021/10/13 09:55:00 2021/10/13 10:25:00 America/Los_Angeles Feature Store Summit - The SAME Project: A Cloud Native Approach to Reproducible Machine Learning This presentation is part of the Feature Store Summit
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We live in a time of both feast and famine in machine learning. Large organizations are publishing state-of-the-art models at an ever-increasing rate but most data scientists still face daunting challenges to reproduce the results themselves. Even in the best cases, where a newly forked code runs without syntax errors (often not the case), this only solves a part of the problem as the pipelines used to run the models are often completely excluded.

The Self-Assembling Machine Learning Environment (SAME) project is a new Kubernetes and Kubeflow project and community around a common goal: creating tooling that allows for quick ramp-up, seamless collaboration and efficient scaling. This talk will discuss our initial public release, done in collaboration with data scientists from across the spectrum, where we are going next and how people can use our learnings in their own practices. https://us02web.zoom.us/j/84897688570 false MM/DD/YYYY 30 OPAQUE apQfLtmRnzOiFbgoNmal132273

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https://us02web.zoom.us/j/84897688570

Featurestore.org, featurestoreorg@gmail.com

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