
Yet, somebody has to glue all the “data science” and “software” parts together. Take the trained model and make it work on quality production environment. Schedule batch jobs recalculating insight tables. Serve model in real time and monitor its performance in the wild. And this is the exact area in which machine learning engineer shines.
The short feedback loop and malleability of today’s software comes with a price. While software development can be much more playful today, it’s also easier to hack before we think, and it can create a lot of problems. Great software still does require a lot of thought, and with ease we lose rigor.