11. Deployment

Deployment makes your product available to users. This chapter covers deployment strategies for local AI products, considering the unique requirements of AI workloads including hardware constraints, model loading, and privacy considerations.

Deployment Targets

Local AI products can be deployed to various targets:

• Local machines — Direct installation on user hardware
• Local servers — For team or organization use
• Cloud instances — For remote access with local AI privacy
• Edge devices — For embedded or IoT scenarios

Each target has different requirements. Desktop deployment prioritizes simplicity. Server deployment prioritizes reliability and monitoring. Edge deployment prioritizes resource efficiency.

Docker Deployment

Docker provides consistent deployment across environments. For local AI products, Docker offers a practical balance.

# docker-compose.yml
version: '3.8'

services:
  localai:
    build: .
    ports:
      - "8000:8000"
    volumes:
      - ./data:/app/data
      - ./config:/app/config
    environment:
      - MODEL_PATH=/app/models/default
      - MAX_RESULTS=20
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]

GPU Support

Local AI inference often benefits from GPU acceleration. Docker requires NVIDIA Container Toolkit for GPU access. Test that GPU access works correctly in your deployment environment.

# Verify GPU access in Docker
docker run --rm --gpus all nvidia/cuda:11.8-base-ubuntu22.04 nvidia-smi

Security Considerations

Local AI products process potentially sensitive data. Consider security implications:

• Data at rest encryption for stored documents
• Access controls for API endpoints
• Input sanitization for user-provided queries
• Model isolation for multi-tenant scenarios

Document security features and configuration in your deployment documentation.

Monitoring and Logging

Production deployments need observability. Log significant events, track usage patterns, and monitor system health.

# src/utils/logging.py
import logging
import structlog

def configure_logging(log_level: str = "INFO"):
    logging.basicConfig(
        format="%(message)s",
        level=getattr(logging, log_level.upper())
    )
    
    structlog.configure(
        processors=[
            structlog.stdlib.filter_by_level,
            structlog.stdlib.add_logger_name,
            structlog.stdlib.add_log_level,
            structlog.processors.TimeStamper(fmt="iso"),
            structlog.processors.JSONRenderer()
        ],
        wrapper_class=structlog.stdlib.BoundLogger,
        context_class=dict,
        logger_factory=structlog.stdlib.LoggerFactory(),
        cache_logger_on_first_use=True,
    )

Deployment Verification

After deployment, verify everything works correctly. Test the primary workflow end-to-end, check system resource usage, and confirm logs are flowing correctly. Document any issues encountered and resolved.