Behind the scenes of training, managing and deploying machine learning models

Abstract: “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!

Bio: Piotr is a Data Scientist at His PhD thesis was centered on data visualization and complex networks techniques for quantum states. After performing research at MIT in Cambridge (MA), ICFO in Barcelona and ISI Foundation in Turin, he focused on data science, social networks analysis and hierarchical clustering. His expertise was welcomed at Caltech, Barcelona Python Meetup and Bay Area D3.js User Group.