feat: enhance model training and deployment scripts with time-weighted sampling

- Updated `train_model.py` and `train_mlx_model.py` to include a time weight decay parameter for improved sample weighting during training.
- Modified dataset generation to incorporate sample weights based on time decay, enhancing model performance.
- Adjusted deployment scripts to support new backend options and improved error handling for model file transfers.
- Added new entries to the training log for better tracking of model performance metrics over time.
- Included ONNX model export functionality in the MLX filter for compatibility with Linux servers.

models/training_log.json

@@ -31,5 +31,19 @@
     "samples": 1696,
     "features": 21,
     "model_path": "models/lgbm_filter.pkl"
+  },
+  {
+    "date": "2026-03-01T21:03:56.314547",
+    "auc": 0.5406,
+    "samples": 1707,
+    "features": 21,
+    "model_path": "models/lgbm_filter.pkl"
+  },
+  {
+    "date": "2026-03-01T21:12:23.866860",
+    "auc": 0.502,
+    "samples": 3269,
+    "features": 21,
+    "model_path": "models/lgbm_filter.pkl"
   }
 ]