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Towards Reproducible Machine Learning

Posted Mar 28
# Model serving
# Systems and Architecture
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SPEAKER
David Aronchick
David Aronchick
David Aronchick
Partner, Program Manager @ Microsoft

David leads Open Source Machine Learning Strategy at Microsoft Azure. Previously, he led product management for Kubernetes, launched Google Kubernetes Engine and co-founded the Kubeflow project while at Google. David has also worked at Amazon, Chef, and co-founded three startups.

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David leads Open Source Machine Learning Strategy at Microsoft Azure. Previously, he led product management for Kubernetes, launched Google Kubernetes Engine and co-founded the Kubeflow project while at Google. David has also worked at Amazon, Chef, and co-founded three startups.

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SUMMARY

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 the average data scientist 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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