During my work at Safran, I realized that while there exist a lot of articles talking about the math and statistics behind ML/AI, few are comprehensive enough to mention the pitfalls that a lot of people fall into while introducing ML into an enterprise.

“In theory, there is no difference between theory and practice — in practice there is.”

This series of articles is meant for two kinds of people in mind, a younger me — someone who knows the technical fundamentals of machine learning and statistics, has gained experience from Kaggle challenges, but has not worked in a large organization that hasn’t set up an infrastructure for ML — or someone who is looking to add value to their business by using ML/Data Science. …