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The SAME Project: A Cloud Native Approach to Reproducible Machine Learning

Posted Jul 28, 2021 | Views 314
# Machine Learning
# Azure Innovations
# Azure.microsoft.com
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David Aronchick
Program Manager, Azure Innovations @ Azure

David leads works in the Azure Innovation Office on Machine Learning. This means he spends most of my time helping humans to convince machines to be smarter. He am only moderately successful at this.

Previously, he led product management for Kubernetes on behalf of Google, launched Google Kubernetes Engine, and co-founded the Kubeflow project. He has also worked at Microsoft, Amazon and Chef and co-founded three startups.

When not spending too much time in service of electrons, he can be found on a mountain (on skis), traveling the world (via restaurants) or participating in kid activities, of which there are a lot more than he remembers than when he was that age.

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Demetrios Brinkmann
Chief Happiness Engineer @ MLOps Community

At the moment Demetrios is immersing himself in Machine Learning by interviewing experts from around the world in the weekly MLOps.community meetups. Demetrios is constantly learning and engaging in new activities to get uncomfortable and learn from his mistakes. He tries to bring creativity into every aspect of his life, whether that be analyzing the best paths forward, overcoming obstacles, or building lego houses with his daughter.

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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 faces 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 project and community around a common goal: creating tooling that allows for quick ramp-up, seamless collaboration, and efficient scaling. David is so thrilled to discuss their initial public release, done in collaboration with data scientists from across the spectrum, where they are going next, and how people can use their learnings in their own practices.

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