1 Introduction
Machine Learning and Artificial Intelligence were born in academia in the 1950s. Applied statistics, the precursor of the modern Data Science, has even a longer history. For decades, these techniques have been used in a myriad of business applications from financial forecasting to chemical engineering.
In the past when ML and data science were considered as advanced techniques used by PhD-level scientists in specialized applications, there was no expectation that infrastructure would exist. Building specialized applications required a special level of effort, knowledge, and patience. Today, the world is a different place. You don’t need a PhD to develop a jaw-dropping computer vision demo or a robust model for predicting sales. It is reasonable to expect that integrating such models in the surrounding business shouldn’t require a PhD in systems engineering.