front matter
preface
I can’t pin down a moment or weave a convincing anecdote that explains how I came to realize that writing a book about how to manage a machine-learning project would be a good thing to do. The gist of it is that sometime in 2019 I realized that I was talking to a lot of people who had started an ML project and were in trouble with it, and usually I knew why.
There wasn’t one common malady or even a single theme, rather failures seemed to come from lots of different directions. Disparate as the failings of these projects were, there was a common cause at work here. The folks leading these projects were talented, clever, articulate, and skilled, but they were inexperienced.
I was very lucky in the timing of my career. I got into ML when it was on the edge of applications. In the late 1990’s, ML was out there in the wild, and we could do real things with our three-layer perceptron’s and decision trees. It was much harder to deliver, algorithms needed to be coded by hand, data was vanishing rare, and everything ran sooooo slowly. Most of all, ML skills were as rare as the projects that needed them and applied ML was seen as R&D. For me this meant that I had the opportunity to develop and work on project after project. Most of them failed—but the ones that did come off really, really, really came off.