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.
In this episode
Program Manager, Azure Innovations, Microsoft
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. David is only moderately successful at this. Previously, David led product management for Kubernetes on behalf of Google, launched Google Kubernetes Engine, and co-founded the Kubeflow project. David 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.
Demetrios is one of the main organizers of the MLOps community and currently resides in a small town outside Frankfurt, Germany. He is an avid traveller who taught English as a second language to see the world and learn about new cultures. Demetrios fell into the Machine Learning Operations world, and since, has interviewed the leading names around MLOps, Data Science, and ML. Since diving into the nitty-gritty of Machine Learning Operations he felt a strong calling to explore the ethical issues surrounding ML. When he is not conducting interviews you can find him making stone stacking with his daughter in the woods or playing the ukulele by the campfire.