Thursday, October 28, 2021
We live in a time of both feast and famine in machine learning. Large orgs are publishing state-of-the-art models at an ever-increasing rate but data scientists face daunting challenges reproducing results themselves. Even in the best cases, where a newly forked code runs without syntax errors, 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. 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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