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. In deepsense.io he works on object detection, classification and image processing. He is specialising in implementing modern, advanced architectures of artificial neural networks and applying them to real-life problems.
Title: Behind the scenes of training, managing and deploying machine learning models
“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?
We had the same problems when running research and multiple commercial machine/deep learning projects. Based on our experience, we have distilled a number of best practices that can significantly improve your team’s performance.
We will guide you through the process of building a robust data science pipeline by using a range of technologies (e.g. Git, Docker or Neptune – our in-house tool for managing machine learning experiments).
Join our session and also share your best practices with us.
Let’s do data science the right way!