Machine learning (ML) is exciting. It’s fun, challenging, creative, and intellectually stimulating. It also makes money for companies, autonomously tackles overwhelmingly large tasks, and removes the burdensome task of monotonous work from people who would rather be doing something else.
ML is also ludicrously complex. From thousands of algorithms, hundreds of open source packages, and a profession of practitioners required to have a diverse skill set ranging from data engineering (DE) to advanced statistical analysis and visualization, the work required of a professional practitioner of ML is truly intimidating. Adding to that complexity is the need to be able to work cross-functionally with a wide array of specialists, subject-matter experts (SMEs), and business unit groups—communicating and collaborating on both the nature of the problem being solved and the output of the ML-backed solution.