Monitor AI Model Health with Prometheus Client and BentoML

                      
                     
"""
Production implementation for monitoring AI model health.
Utilizes Prometheus for metrics collection and BentoML for deployment.
"""
from typing import Dict, Any, List, Tuple
import os
import logging
import time
import prometheus_client
from prometheus_client import CollectorRegistry, Gauge
from prometheus_client.exposition import basic_auth_handler
import bentoml
from bentoml import Service

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

class Config:
    """
    Configuration settings for the application.
    Loads environment variables for configuration.
    """
    model_name: str = os.getenv('MODEL_NAME', 'default_model')
    prometheus_user: str = os.getenv('PROMETHEUS_USER', 'user')
    prometheus_pass: str = os.getenv('PROMETHEUS_PASS', 'pass')

# Prometheus metrics registry
registry = CollectorRegistry()
model_health_gauge = Gauge('ai_model_health', 'Health of AI model', ['model_name'], registry=registry)

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')  # Input validation
    return True  # Data is valid

def fetch_model_metrics() -> Tuple[float, float]:
    """Fetch metrics for the AI model.
    
    Returns:
        Tuple of (accuracy, latency)
    """
    # Mock fetching metrics
    accuracy = 0.95  # 95% accuracy
    latency = 120.0  # 120 ms latency
    return accuracy, latency

def update_health_metrics(model_name: str, accuracy: float, latency: float) -> None:
    """Update Prometheus health metrics.
    
    Args:
        model_name: Name of the model
        accuracy: Model accuracy
        latency: Model latency
    """
    model_health_gauge.labels(model_name).set(accuracy)  # Set accuracy
    logger.info(f'Updated metrics for {model_name}: accuracy={accuracy}, latency={latency}')  # Log the update

def monitor_model_health() -> None:
    """Monitor the health of the AI model.
    Collects metrics and updates Prometheus.
    """
    try:
        accuracy, latency = fetch_model_metrics()  # Fetch metrics
        update_health_metrics(Config.model_name, accuracy, latency)  # Update metrics
    except Exception as e:
        logger.error(f'Error monitoring model health: {e}')  # Log error

def start_prometheus_server() -> None:
    """Start Prometheus metrics server.
    """
    prometheus_client.start_http_server(8000)  # Start server on port 8000
    logger.info('Prometheus metrics server started on port 8000')  # Log server start

async def process_batch(data: List[Dict[str, Any]]) -> None:
    """Process a batch of input data.
    
    Args:
        data: List of input data dictionaries
    """
    for record in data:
        try:
            validate_input(record)  # Validate input
            # Perform model inference and update model health
            monitor_model_health()  # Monitor model health
        except ValueError as ve:
            logger.warning(f'Validation error: {ve}')  # Log validation error
        except Exception as e:
            logger.error(f'Error processing record: {e}')  # Log processing error

class ModelHealthService:
    """Service class for managing model health monitoring.
    """
    def __init__(self) -> None:
        self.service = Service(name=Config.model_name)  # Initialize BentoML service

    def setup_routes(self) -> None:
        """Setup API routes for the model health service.
        """
        @self.service.api(input=bentoml.io.JSON(), output=bentoml.io.JSON())
        async def health_check(data: Dict[str, Any]) -> Dict[str, Any]:
            """Health check endpoint.
            
            Args:
                data: Input data for health check
            Returns:
                Health status of the model
            """
            validate_input(data)  # Validate input
            monitor_model_health()  # Monitor model health
            return {'status': 'healthy'}  # Return health status

if __name__ == '__main__':
    start_prometheus_server()  # Start Prometheus server
    model_service = ModelHealthService()  # Create model health service
    model_service.setup_routes()  # Setup routes
    # Start the BentoML service
    bentoml.run(model_service.service)