Deployment and Serving Index

Making trained models accessible in production.

Notes

• Serving Patterns — batch, online REST/gRPC, streaming, and edge inference patterns with latency/throughput trade-offs
• Model Compression — quantization, pruning, knowledge distillation, and ONNX export for efficient deployment
• Rollout Strategies — canary, blue-green, shadow mode, and feature-flag-controlled progressive rollouts

Links

• ↑ ML Engineering
• Prev: ← 05 — Model Development
• Next: 07 — Monitoring & Observability →