Roman Rožník Kiwi.com, Data Science Slave

No doubt machine learning is a hot topic in recent years, it seem's everybody can easily become a data scientist and do ML within few lines of code. Reality is much harder. Understanding the problem, preparing right training data, cleaning them, designing features, interpretability / complexity of the model, defining right metrics, looking at false positives / negatives, interpretation of ML results or AB tests - those are topics highly tied with data science that are often overlooked and underrate. I'd like to emphasize that those are very important and ML itself is just a one small piece of complex data science puzzle. Not a single line of a code in this talk.