From notebook hell to container heaven

Here's a bitter pill to swallow in modern Machine Learning. Companies are investing heavily in ML. But most real-life ML applications, while looking shiny (no pun intended) in the frontend, are often nothing more than a bunch of notebooks duct-taped together, which are hard to maintain and hard to add new features to. This slows development teams down and stops innovation. There has been much debate online about notebooks and their pros and cons.