Machine learning algorithms work really well in situations where exists lots of ground truth data, but if you don’t have enough cases for machine learning model to train on, it is unlikely that it will be able to generalize the features needed to be successful. Except good data there are various other factors that impact the success of an machine learning project and you should evaluate really well if your organization fulfills certain requirements before you jump on a machine learning endeavor.

Usually the machine learning talks are about interesting use-cases, trendy algorithm, how you can apply ML to create a competitive advantage and rarely about what can go wrong when going on the machine learning path. In this talk, I will try try to fill that gap by discussing some sounding machine learning failures, what do they have in common, and some hints how to prevent them from failing.