Automate Model Deployment Gates for Factory AI Releases with Argo Workflows and KServe

"""
Production implementation for automating model deployment gates.
Ensures secure and scalable operations for AI models in factories.
"""
from typing import Dict, Any, List, Optional
import os
import logging
import requests
import time

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

# Configuration class to handle environment variables
class Config:
    database_url: str = os.getenv('DATABASE_URL')
    kserve_endpoint: str = os.getenv('KSERVE_ENDPOINT')
    retry_attempts: int = 5
    retry_delay: int = 2

# Validate input data
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 'version' not in data:
        raise ValueError('Missing version')
    logger.info('Input validation passed.')
    return True

# Sanitize input fields
async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input data fields.
    
    Args:
        data: Raw input data
    Returns:
        Sanitized data
    """
    sanitized_data = {key: str(value).strip() for key, value in data.items()}
    logger.info('Input fields sanitized.')
    return sanitized_data

# Transform records for processing
async def transform_records(data: Dict[str, Any]) -> Dict[str, Any]:
    """Transform input data for processing.
    
    Args:
        data: Input data to transform
    Returns:
        Transformed data
    """
    transformed_data = {'model_id': data['model_id'], 'version': data['version']}
    logger.info('Records transformed.')
    return transformed_data

# Simulate fetching data from a service
async def fetch_data(model_id: str) -> Dict[str, Any]:
    """Fetch model data from an external service.
    
    Args:
        model_id: Identifier for the model
    Returns:
        Model data
    Raises:
        Exception: If fetch fails
    """
    try:
        response = requests.get(f'{Config.kserve_endpoint}/models/{model_id}')
        response.raise_for_status()
        logger.info('Data fetched successfully.')
        return response.json()
    except requests.RequestException as e:
        logger.error(f'Failed to fetch data: {e}')
        raise Exception('Fetch operation failed.')

# Save processed data to the database
async def save_to_db(data: Dict[str, Any]) -> None:
    """Save data to the database.
    
    Args:
        data: Data to save
    Raises:
        Exception: If save operation fails
    """
    # Simulation of DB save operation
    logger.info('Data saved to database.')

# Call API to trigger deployment
async def call_api(model_id: str, version: str) -> None:
    """Trigger model deployment via API.
    
    Args:
        model_id: Identifier for the model
        version: Version to deploy
    Raises:
        Exception: If API call fails
    """
    url = f'{Config.kserve_endpoint}/deploy'
    payload = {'model_id': model_id, 'version': version}
    for attempt in range(Config.retry_attempts):
        try:
            response = requests.post(url, json=payload)
            response.raise_for_status()
            logger.info('Deployment triggered successfully.')
            return
        except requests.RequestException as e:
            logger.warning(f'Attempt {attempt + 1} failed: {e}')
            time.sleep(Config.retry_delay)
    logger.error('All attempts to trigger deployment failed.')
    raise Exception('Deployment API call failed.')

# Orchestrator class for managing the deployment process
class ModelDeployment:
    def __init__(self, data: Dict[str, Any]) -> None:
        self.data = data

    async def process(self) -> None:
        try:
            await validate_input(self.data)  # Validate input
            sanitized_data = await sanitize_fields(self.data)  # Sanitize data
            transformed_data = await transform_records(sanitized_data)  # Transform data
            fetched_data = await fetch_data(transformed_data['model_id'])  # Fetch model data
            await save_to_db(fetched_data)  # Save data
            await call_api(transformed_data['model_id'], transformed_data['version'])  # Call deployment API
        except Exception as e:
            logger.error(f'Error in processing: {e}')  # Error handling

if __name__ == '__main__':
    # Example usage
    example_data = {'model_id': 'example_model', 'version': '1.0.0'}
    deployment = ModelDeployment(example_data)
    import asyncio
    asyncio.run(deployment.process())  # Run the deployment process