｢ The proposal centers on a lightweight “ML proxy” within the kernel. This proxy would expose structured data from kernel subsystems, such as internal state or performance metrics, and receive recommendations from a user-space ML model. Training, model execution, and experimentation would remain outside the kernel, ensuring the kernel retains full control over the application of recommendations ｣
https://linuxiac.com/new-proposal-explores-machine-learning-assistance-for-linux-kernel-behavior/
