The short answer is that it does not internalize updates itself. It's an interesting question of design. Of course, an updated model matters when answering queries in the serving layer. When just being used to determine how a new input changes the model, it's not necessarily important to have consumed prior updates to compute a good-enough update. From an implementation perspective, it makes things significantly simpler; the model updates, in general, are intended to be computed in a distributed way with Spark. If they were also updating the model, it'd be hard and slow to also coordinate those in-memory updates meaningfully. The price of course is that the speed layer itself isn't learning in real time, even if that isn't actually nearly as important as the serving layer doing so. Now, interestingly, for ALS, the model itself is so big that the updates themselves can't be computed in a distributed way. It's actually done serially on the driver, on one big copy in memory. So it would be realistic to apply model updates as it goes. I'm going to file this as a "to do" to think about further since it's also the model where it matters more than others. It also occurs to me that, for this reason, the driver should multi-thread its computation of the updates for ALS. Also a to-do
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