I&#x27;m not a data scientist per se, but I&#x27;ve been working with some (boss and co-worker) to get some stuff operationalized and into production, so I&#x27;ve been responsible for generating inputs, helping analyze&#x2F;visualize outputs, and building linear optimization models, so I&#x27;ve got some very basic experience.<p>As I understand it, one of the pitfalls of automatic tuning is that it becomes hard to account for seasonality and you will likely end up with useless parameters - for instance a customer ID is rarely a good parameter to optimize on, even as a categorical variable, except in very specific cases.  It is probably a proxy variable for one or more other ones that you need to tease out of the rest of the data.<p>(warning, potentially me talking nonsense coming up) Automatic tuning is no substitute for a talented analyst who knows the data well and understands the goal.  But if you&#x27;ve got hundreds to millions of parameters, you may not have another choice really.