Add model training pipeline with MLflow tracking

Overview

Setup automated model training pipeline with experiment tracking.

Components

1. Data preprocessing pipeline
2. Feature engineering
3. Model training (scheduled daily)
4. Model evaluation and validation
5. Model registry and versioning

MLflow Integration

• Track hyperparameters
• Log metrics (precision, recall, F1)
• Store model artifacts
• Compare experiment runs

Infrastructure

• Kubernetes CronJob for training
• S3 for model artifacts
• MLflow server for tracking

Acceptance Criteria

• Automated daily training runs
• Model versioning with semantic versioning
• Automatic deployment of models with accuracy > 90%
• Rollback capability to previous model version