SIENTIAPDE-1255: Refactor data quality gates to training focused metrics and repositories. This commit removes the data quality gates and filters, focusing on training-specific metrics and data repositories. It also updates the README to reflect these changes, including new training metrics and a streamlined data services section.

README.md

@@ -167,17 +167,18 @@ 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 MLFlow operations
-  - `model_repository.py`: MLFlow model operations and retraining
-- **Filters**: Data quality validation and MLFlow response filtering
-  - `conditional_filters.py`: Input data validation filters
-  - `mlflow_filters.py`: MLFlow API response validation filters
+- **Repository**: Data access layer for training operations
+  - `training_repository.py`: Training business logic and operations
+- **Models**: Data models and schemas
+  - `train_model_params.py`: Training parameters model
+  - `train_model_result.py`: Training result model
+  - `experiment_status.py`: Experiment status enum
 - **Key Features**:
   - Environment variable-based configuration with sensible defaults
   - Connection pool management and optimization
   - Security credential management
   - Configuration validation and error handling
-  - Support for MLFlow model flavors
+  - Type-safe data models with validation
 
 ### Data Flow Architecture
 
@@ -814,10 +815,10 @@ The Model Manager system exposes comprehensive Prometheus metrics for operationa
   - Labels: `pod_id`, `model_name`, `pipeline_name`
   - Buckets: [0.01, 0.05, 0.1, 0.2, 0.5, 1.0, 2.0, 5.0, 10.0]
 
-### Data Quality Metrics
-- Filter pass/fail rates through notification system
-- MLFlow API response validation metrics
-- Data quality gate performance tracking
+### Training Metrics
+- Training success/failure rates through notification system
+- Model save performance metrics
+- Experiment status tracking
 
 ## Configuration
 
@@ -887,7 +888,7 @@ The project maintains **99%+ code coverage** with comprehensive unit and integra
 pytest tests/ --cov=model_manager --cov-report=term-missing --cov-report=xml --cov-report=html
 
 # Run specific test file
-pytest tests/activities/test_gates.py -v
+pytest tests/activities/test_training.py -v
 
 # Run with coverage visualization
 pytest tests/ --cov=model_manager --cov-report=xml
@@ -1019,7 +1020,7 @@ model_manager/
 
 4. **Workflow Execution Failures**
    - Review activity error logs and notifications
-   - Check data quality filter configurations
+   - Check training parameter validation errors
    - Verify input data format and required fields
 
 ### Debug Mode