Deployment and Project Integration · Lesson 8
Advanced Deployment, Scaling & LLMOps
Scale with horizontal autoscaling, response and semantic caching, request batching, and routing easy requests to smaller, cheaper models.
When self-hosting open models, inference servers such as vLLM, TGI or Ollama provide efficient batching and quantised models; Kubernetes manages GPU workloads.
Advanced rollout methods include canary releases, A/B tests between prompts or models, and shadow deployments that test a new version on live traffic without showing users.
Key points
- Cache, batch and route to cheaper models
- vLLM/TGI for efficient self-hosting
- Canary and A/B testing for safe rollouts
- Shadow deploys test without user impact