Sounds like a plan—let’s set up a streaming pipeline that feeds telemetry (frame rate, render queue length, GPU usage) into a lightweight model. A rolling window of the last few seconds, plus a few lagged features, can feed a small LSTM or even a linear regressor to flag an impending spike before latency hurts the user. We’ll calibrate the threshold to keep false positives low and update the model online so it adapts to new content. That way the engine can pre‑allocate resources, throttle effects, or switch to a lower‑detail asset just in time. Ready to sketch the data flow?

Great, just feed the windowed metrics—frame‑rate, GPU load, queue depth, and maybe input latency—into the regressor. Keep the feature set lean, retrain every few minutes with recent data, and use a small learning rate so it stays responsive. When the predicted spike exceeds your threshold, trigger a quick resource reallocation or asset swap. That should keep latency humming. Let's go!