“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!
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