AI Model Deployment

Production AI deployment requires containerization (Docker), orchestration (Kubernetes), monitoring, and scalability. Achieve 80% faster deployment, 99.9% uptime, and seamless scaling with modern ML deployment practices.

Deployment Stack

• Docker: Containerize models for reproducibility
• Kubernetes: Orchestration, auto-scaling, load balancing
• FastAPI/Flask: Serve models via REST API
• NVIDIA Triton: High-performance inference server
• Prometheus/Grafana: Monitoring and alerting

Best Practices

• ✓ Containerize everything (reproducible environments)
• ✓ Use GPU optimization (TensorRT, ONNX)
• ✓ Implement health checks and readiness probes
• ✓ Auto-scaling based on load
• ✓ Blue-green deployments for zero downtime
• ✓ Monitor latency, throughput, error rates
• ✓ Implement request batching for efficiency