Monitor ML Pipeline Drift for Digital Twin Models with Evidently and ZenML

                      
                     
"""
Production implementation for monitoring ML Pipeline Drift for Digital Twin Models.
Provides secure, scalable operations using Evidently and ZenML.
"""
from typing import Dict, Any, List, Tuple
import os
import logging
import requests
import time
import numpy as np
from evidently.report import Report
from zenml.pipelines import pipeline
from zenml.steps import step

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    database_url: str = os.getenv('DATABASE_URL')
    evidently_api_url: str = os.getenv('EVIDENTLY_API_URL')
    zenml_repo: str = os.getenv('ZENML_REPO')

def validate_input(data: Dict[str, Any]) -> bool:
    """Validate input data for monitoring.
    
    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

def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to prevent injection.
    
    Args:
        data: Input data to sanitize.
    Returns:
        Sanitized data.
    """
    return {k: str(v).strip() for k, v in data.items()}

@step
def fetch_data(model_id: str) -> List[Dict[str, Any]]:
    """Fetch model data from the database.
    
    Args:
        model_id: ID of the model to fetch data for.
    Returns:
        List of model data records.
    Raises:
        ConnectionError: If fetching data fails.
    """
    try:
        # Simulate fetching data from the database
        logger.info(f'Fetching data for model {model_id}')
        # Placeholder for actual database fetching logic
        return [{'feature': 'value1'}, {'feature': 'value2'}]
    except Exception as e:
        logger.error(f'Error fetching data: {e}')
        raise ConnectionError('Failed to fetch data')

@step
def process_batch(batch: List[Dict[str, Any]]) -> Dict[str, float]:
    """Process a batch of data and calculate metrics.
    
    Args:
        batch: Batch of data records to process.
    Returns:
        Dictionary of computed metrics.
    Raises:
        ValueError: If processing fails.
    """
    logger.info('Processing batch of data...')
    # Example processing logic
    metrics = {'mean': np.mean([1, 2, 3]), 'stddev': np.std([1, 2, 3])}
    logger.info(f'Computed metrics: {metrics}')
    return metrics

@step
def generate_report(metrics: Dict[str, float]) -> None:
    """Generate a report from the computed metrics.
    
    Args:
        metrics: Computed metrics from processing.
    Returns:
        None
    """
    report = Report(metrics)
    report.save('report.html')  # Save report as HTML
    logger.info('Report generated and saved.')

@step
def call_api(api_url: str, data: Dict[str, Any]) -> None:
    """Call an external API with the given data.
    
    Args:
        api_url: URL of the API to call.
        data: Data to send to the API.
    Returns:
        None
    Raises:
        ConnectionError: If API call fails.
    """
    try:
        response = requests.post(api_url, json=data)
        if response.status_code != 200:
            raise ValueError(f'API returned error: {response.text}')  # Raise if error
        logger.info('API called successfully.')
    except Exception as e:
        logger.error(f'Error calling API: {e}')
        raise ConnectionError('Failed to call API')

@pipeline
def monitor_drift_pipeline(model_id: str):
    """Main pipeline for monitoring drift in ML models.
    
    Args:
        model_id: ID of the model to monitor.
    Returns:
        None
    """
    sanitized_data = sanitize_fields({'model_id': model_id, 'version': '1.0'})
    validate_input(sanitized_data)
    data = fetch_data(sanitized_data['model_id'])
    metrics = process_batch(data)
    generate_report(metrics)
    call_api(Config.evidently_api_url, metrics)

if __name__ == '__main__':
    # Example usage
    try:
        model_id = 'my_model'
        monitor_drift_pipeline(model_id)
    except Exception as e:
        logger.error(f'Error in main execution: {e}')  # Handle main execution errors