I'm a little curious - have you done machine learning work that's made it into a real product?
I tried doing it a bit with my last project. My results were basically terrible. It turns out that getting useful results out of heterogenous, vague, fuzzily-specified real world data is really hard.
I'm tight with a few folks in Search Quality whose job is that sort of data-scientist machine-learning work, and they're really good at it. Y'know what 90% of their daily time is spent on? Compiling golden sets. Labeling training data. Running MapReduces to collect basic statistics about their data set. Running MapReduces to identify representative members of their data set, and outliers that should be excluded. Shoving data into R to visualize it. Futzing with numeric coefficients. Building webpages and tools so they can visualize the data and results, futz with the numbers online, and get feedback in real time. Collecting test sets and running your algorithm against them, and then trying to figure out why your losses are losing.
Machine-learning from a practitioner's POV is not at all like the textbooks and theoretical papers suggest. I'd estimate that less than 10% of one's time goes into the "fun" part of machine learning - brainstorming new signals and writing the code to extract them and feed them into your classifier - and 90% is on the kind of grunt work that hard science grad students do all the time. You get paid well for it, but that's because a lot of the work is really boring and time-consuming. I suspect that I get a far more frequent rush of accomplishment as a mostly-UI guy than the data scientists in my department get.
It's a tool. It works well in some cases, but it can take a lot of effort to get it to work well.
