Track Digital Twin Model Drift with Evidently and MLflow

                      
                     
"""
Production implementation for tracking digital twin model drift using Evidently and MLflow.
Provides secure, scalable operations with data validation and logging.
"""
from typing import Dict, Any, List, Tuple
import os
import logging
import mlflow
import evidently
from evidently import Dashboard
from evidently.model import Dashboard
from evidently.metrics import Metric

# Logging configuration
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Configuration class for environment variables
class Config:
    database_url: str = os.getenv('DATABASE_URL')
    mlflow_tracking_uri: str = os.getenv('MLFLOW_TRACKING_URI')

# Data validation function
async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate input data for model tracking.
    
    Args:
        data: Input data to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'model_id' not in data:
        raise ValueError('Missing model_id')
    if 'version' not in data:
        raise ValueError('Missing version')
    return True

# Data sanitization function
async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to prevent injection attacks.
    
    Args:
        data: Input data to sanitize
    Returns:
        Sanitized data
    """
    sanitized_data = {key: str(value).strip() for key, value in data.items()}
    return sanitized_data

# Data transformation function
async def transform_records(raw_data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Transform raw data records for analysis.
    
    Args:
        raw_data: List of raw data records
    Returns:
        Transformed data records
    """
    transformed_data = []
    for record in raw_data:
        transformed_data.append({
            'model_id': record['model_id'],
            'version': record['version'],
            'drift_score': record.get('drift_score', 0.0),
        })
    return transformed_data

# Fetch data from the database
async def fetch_data(query: str) -> List[Dict[str, Any]]:
    """Fetch data from the database based on the query.
    
    Args:
        query: SQL query to execute
    Returns:
        List of records fetched from the database
    """
    # Placeholder for database fetching logic
    logger.info(f'Executing query: {query}')
    return []  # Replace with actual database fetching code

# Save metrics to MLflow
async def save_to_mlflow(metrics: Dict[str, Any]) -> None:
    """Save metrics to the MLflow tracking server.
    
    Args:
        metrics: Dictionary of metrics to save
    """
    mlflow.log_metrics(metrics)
    logger.info('Metrics logged to MLflow')

# Process batch of data
async def process_batch(data: List[Dict[str, Any]]) -> None:
    """Process a batch of data for model drift tracking.
    
    Args:
        data: List of input data to process
    """
    try:
        for record in data:
            logger.info(f'Processing record: {record}')
            await validate_input(record)
            sanitized_data = await sanitize_fields(record)
            transformed_data = await transform_records([sanitized_data])
            await save_to_mlflow(transformed_data)
    except Exception as e:
        logger.error(f'Error processing batch: {e}')
        raise

# Main orchestrator class
class ModelDriftTracker:
    def __init__(self, config: Config):
        self.config = config
        mlflow.set_tracking_uri(self.config.mlflow_tracking_uri)

    async def track_drift(self, model_data: List[Dict[str, Any]]) -> None:
        """Track model drift based on input data.
        
        Args:
            model_data: List of model data to analyze
        """
        await process_batch(model_data)
        logger.info('Model drift tracking completed')

# Main block
if __name__ == '__main__':
    config = Config()
    tracker = ModelDriftTracker(config)
    example_data = [
        {'model_id': 'model_1', 'version': 'v1.0'},
        {'model_id': 'model_2', 'version': 'v1.1'},
    ]
    # Example usage
    import asyncio
    asyncio.run(tracker.track_drift(example_data))