Model Deployment & Serving¶

Coming soon.

• Batch vs. real-time serving — which one a given use case actually needs
• Canary and shadow rollout for a new model version before it takes full traffic
• Rollback: reverting a bad deployment fast, with minimal blast radius
• The ML-specific layer on top of what Infrastructure already covers for deployment in general

Related¶

• Experiment Tracking & Model Registry — the registry entry that gets promoted to a deployment
• Model Monitoring & Drift — what tells you a deployed model needs a rollback or a retrain
• Deployment Strategies — general canary/blue-green patterns this topic will specialize for models

Part of MLOps → AI Engineering.