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“The model was working just fine two weeks ago, but now I can’t reproduce it!”
“Bob’s on vacation – how do I run his model?”
“Is my neural network useless or should I continue tweaking its parameters?”
Have you ever heard any of the above before?
Pawel Subko's team had the same problems when running research and multiple commercial machine/deep learning projects. In this talk you will discover a number of best practices that can significantly improve your team’s performance, based on Pawel's experiences.
You will explore the process of building a robust data science pipeline by using a range of technologies (e.g. Git, Docker or Neptune – Pawel's in-house tool for managing machine learning experiments).
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Behind the scenes of training, managing and deploying machine learning models
Pawel Subko is a Data Scientist at deepsense.io. He holds a masters degree in Mathematics from the University of Warsaw. He has published papers on partial differential equations and did research at Charles University in Prague.