feat(ml): implement training stage with MLflow tracking and model wrappers

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- Create RandomForestModel and XGBoostModel wrappers with class weight support
- Implement temporal and random train/val/test splitting
- Add MLflow experiment tracking with full parameter and metric logging
- Create evaluation module for confusion matrix, feature importance, and classification reports
- Implement model training with sklearn/xgboost flavor logging and optional registry registration
- Store training run metadata in PostgreSQL
- Wire training stage into pipeline.py orchestrator
- Support both RandomForest and XGBoost models with configurable hyperparameters

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Marko Djordjevic 2026-02-15 14:22:19 +01:00

parent 16763b967e

commit f4c0f9a836

8 changed files with 900 additions and 14 deletions

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services/ml/training/__init__.py Normal file

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