For those wanting more machine learning in the kernel, Viacheslav Dubeyko has posted a new in-kernel library for that purpose.

What is the goal of using ML models in Linux kernel? The main goal is to employ ML models for elaboration of a logic of particular Linux kernel subsystem based on processing data or/and an efficient subsystem configuration based on internal state of subsystem. As a result, it needs: (1) collect data for training, (2) execute ML model training phase, (3) test trained ML model, (4) use ML model for executing the inference phase. The ML model inference can be used for recommendation of Linux kernel subsystem configuration or/and for injecting a synthesized subsystem logic into kernel space (for example, eBPF logic).

It is rigorously undocumented and there are no real users, so it's not entirely clear what the purpose is, but there are undoubtedly interesting things that could be done with it.