LLMOps Guide

Model Deployment

Ray Serve Configuration

apiVersion: ray.io/v1alpha1
kind: RayService
metadata:
  name: llm-inference
spec:
  serviceUnhealthySecondThreshold: 300
  deploymentUnhealthySecondThreshold: 300
  serveDeployments:
    - name: llm-deployment
      numReplicas: 2
      rayStartParams:
        num-cpus: "16"
        num-gpus: "1"
      containerConfig:
        image: llm-server:latest
        env:
          - name: MODEL_NAME
            value: "llama2-7b"
          - name: BATCH_SIZE
            value: "4"

Model Monitoring

Prometheus Rules

Performance Optimization

Triton Inference Server

Best Practices

  1. Model Management

    • Version control

    • A/B testing

    • Canary deployment

    • Model registry

  2. Observability

    • Performance metrics

    • Token usage

    • Response quality

    • Cost tracking

  3. Optimization

    • Quantization

    • Batching

    • Caching

    • Load balancing

  4. Security

    • Input validation

    • Output filtering

    • Rate limiting

    • Access control

Last updated