Nice idea—just load the data with a streaming API, buffer it in Redis, and run a lightweight model on the edge so latency stays low. Use a time‑window feature extraction for wind and pressure, feed that into a neural net that predicts optimal altitudes, and then feed the path back into your map. Keep the model quantized, so it runs fast on a GPU or even a Raspberry Pi if you’re testing on the ground. Add a little RL loop to let the planner adapt to new weather patterns over time, and you’ll have a smooth, cloud‑learning GPS in no time.