Package Industrial ML Services with BentoML and Docker SDK

                      
                     
"""
Production implementation for packaging industrial machine learning services using BentoML and Docker SDK.
Provides secure, scalable operations in a cloud-native environment.
"""
from typing import Dict, Any, List
import os
import logging
import time
import requests
import bentoml
from bentoml import env, artifacts, BentoService
from bentoml.adapters import DataframeInput

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

class Config:
    """Configuration class for environment variables."""
    database_url: str = os.getenv('DATABASE_URL', 'sqlite:///:memory:')
    model_name: str = os.getenv('MODEL_NAME', 'default_model')

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 'input_data' not in data:
        raise ValueError('Missing `input_data` field')
    return True

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

def fetch_data(url: str) -> List[Dict[str, Any]]:
    """Fetch data from an external API.
    
    Args:
        url: The API endpoint to fetch data from
    Returns:
        List of records retrieved from API
    Raises:
        Exception: If fetching data fails
    """
    try:
        response = requests.get(url)
        response.raise_for_status()  # Raise an error for bad responses
        return response.json()
    except requests.exceptions.RequestException as e:
        logger.error(f"Failed to fetch data: {e}")
        raise

def transform_records(records: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Transform records into a suitable format for processing.
    
    Args:
        records: List of records to transform
    Returns:
        Transformed records
    """
    return [{'data': record['input_data']} for record in records]

def process_batch(data: List[Dict[str, Any]]) -> Dict[str, Any]:
    """Process a batch of data.
    
    Args:
        data: List of data records to process
    Returns:
        Processed results
    """
    results = {'processed': [], 'errors': []}
    for item in data:
        try:
            result = item['data']  # Placeholder for actual ML model prediction
            results['processed'].append(result)
        except Exception as e:
            logger.error(f"Error processing item {item}: {e}")
            results['errors'].append(str(e))
    return results

# Define the BentoML service
@artifacts([bentoml.artifacts.PickleArtifact('model')])
class MLService(BentoService):
    @bentoml.api(DataframeInput)
    def predict(self, df):
        """Predict method for ML service.
        
        Args:
            df: Input dataframe
        Returns:
            Predictions from the model
        """
        return process_batch(df.to_dict(orient='records'))

def main():
    # Load model
    logger.info('Loading model...')
    model = bentoml.load(Config.model_name)
    service = MLService()
    service.pack('model', model)
    # Save the service
    service.save()
    logger.info('Service packaged successfully.')

if __name__ == '__main__':
    main()  # Run the service