> Your best engineers will tell you that your fancy new ML system needs to have a silent, behind-the-scenes _heuristic_ system backing it up. This should have

> Your best engineers will tell you that your fancy new ML system needs to have a silent, behind-the-scenes _heuristic_ system backing it up. This should have been in guide somewhere. Makes a lot more sense than the "data not updated" example, which is a bit too narrow. > So between developing the heuristic system and chasing down regressions in unit testing, it turns out ML isn't going to save you 90% of your engineering time. There are 2 rules about whether to use ML. First, there has to be some underlying rules for the machine to learn. Second, you only want ML if it's harder to figure out the rules themselves. Of course you can bend the second rule a bit if you are a ML expert (and maybe even have the heuristic system lying around already).