“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!