SIENTIAPDE-1255: Implement MLFlow artifact management and model persistence

This commit introduces a new model_repository.py to handle MLFlow artifact generation and model persistence. It also updates the README to reflect this change and modifies training_repository.py to separate training and MLFlow operations.

README.md

@@ -166,8 +166,9 @@ The Model Manager system uses a Temporal-based workflow architecture with clear
 
 #### **Data Services (`model_manager/utils/`)**
 - **Connectors Config**: Environment variable-based configuration management
-- **Repository**: Data access layer for training operations
+- **Repository**: Data access layer for training and MLFlow operations
   - `training_repository.py`: Training business logic and operations
+  - `model_repository.py`: MLFlow artifact generation and model persistence
 - **Models**: Data models and schemas
   - `train_model_params.py`: Training parameters model
   - `train_model_result.py`: Training result model
@@ -959,7 +960,8 @@ model_manager/
 │   │   └── experiment_status.py  # Experiment status enum
 │   └── repository/         # Data access layer
 │       ├── __init__.py
-│       └── training_repository.py # Training business logic
+│       ├── training_repository.py # Training business logic
+│       └── model_repository.py    # MLFlow artifact management
 ├── metrics.py               # Prometheus metrics definitions
 └── __init__.py
 ```