Orchestrate Industrial Model Retraining Pipelines for Digital Twins with Kubeflow and MLflow

                      
                     
"""
Production implementation for orchestrating industrial model retraining pipelines using Kubeflow and MLflow.
Provides secure, scalable operations for digital twins.
"""
from typing import Dict, Any, List, Optional
import os
import logging
import requests
import backoff
from sqlalchemy import create_engine, text
from sqlalchemy.orm import sessionmaker

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

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

# Create a database engine with connection pooling
engine = create_engine(Config.database_url, pool_size=10, max_overflow=20)
Session = sessionmaker(bind=engine)

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data.
    
    Args:
        data: Input to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'model_id' not in data:
        raise ValueError('Missing model_id')
    if 'parameters' not in data:
        raise ValueError('Missing parameters')
    return True

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

@backoff.on_exception(backoff.expo, requests.exceptions.RequestException, max_tries=5)
async def fetch_data(api_url: str) -> List[Dict[str, Any]]:
    """Fetch data from an external API.
    
    Args:
        api_url: URL to fetch data from
    Returns:
        List of data records
    Raises:
        Exception: If the fetch fails
    """
    response = requests.get(api_url)
    response.raise_for_status()  # Raise an error for bad responses
    return response.json()

async def save_to_db(data: List[Dict[str, Any]]) -> None:
    """Save data records to the database.
    
    Args:
        data: List of records to save
    Raises:
        Exception: If saving fails
    """
    with Session() as session:
        for record in data:
            session.execute(text("INSERT INTO models (id, parameters) VALUES (:id, :parameters)"), record)
        session.commit()  # Commit the transaction

async def call_api(api_url: str, payload: Dict[str, Any]) -> Dict[str, Any]:
    """Call an external API with payload for retraining.
    
    Args:
        api_url: URL to call
        payload: Data to send
    Returns:
        Response from the API
    Raises:
        Exception: If the API call fails
    """
    response = requests.post(api_url, json=payload)
    response.raise_for_status()  # Raise an error for bad responses
    return response.json()

async def process_batch(batch: List[Dict[str, Any]]) -> None:
    """Process a batch of records for retraining.
    
    Args:
        batch: List of records to process
    Raises:
        Exception: If processing fails
    """
    try:
        # Sanitize and validate each record
        for record in batch:
            await validate_input(record)
            sanitized_record = await sanitize_fields(record)
            await save_to_db([sanitized_record])  # Save sanitized record
            # Call the retraining API
            await call_api(Config.mlflow_tracking_uri, sanitized_record)
    except Exception as e:
        logger.error(f"Error processing batch: {e}")

async def aggregate_metrics(model_id: str) -> Dict[str, float]:
    """Aggregate metrics for a specific model.
    
    Args:
        model_id: ID of the model to aggregate metrics for
    Returns:
        Aggregated metrics
    Raises:
        Exception: If aggregation fails
    """
    with Session() as session:
        result = session.execute(text("SELECT AVG(metric) FROM metrics WHERE model_id = :model_id"), {'model_id': model_id})
        return {'average_metric': result.scalar()}

# Main orchestrator class
class ModelRetrainingOrchestrator:
    def __init__(self):
        logger.info('ModelRetrainingOrchestrator initialized')

    async def run(self, data: List[Dict[str, Any]]) -> None:
        """Run the complete workflow for model retraining.
        
        Args:
            data: Input data for retraining
        """
        logger.info('Starting model retraining workflow')
        await process_batch(data)  # Process the input data
        logger.info('Model retraining workflow completed')

if __name__ == '__main__':
    # Example usage
    orchestrator = ModelRetrainingOrchestrator()
    sample_data = [{'model_id': '123', 'parameters': {'param1': 'value1'}}]  # Sample input
    # Run the orchestrator with the sample data
    import asyncio
    asyncio.run(orchestrator.run(sample_data))