ML Ops: Machine Learning as an Engineering Discipline

So, your company decided to invest in machine learning. You have a talented team of Data Scientists churning out models to solve important problems that were out of reach just a few years ago. All performance metrics are looking great, the demos cause jaws to drop and executives to ask how soon you can have a model in production. It should be pretty quick, you think. After all, you already solved all the advanced scienc-y, math-y problems, so all that's left is routine IT work.