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Chapter 70: Deployment & Serving

Turn trained models into reliable services. This chapter builds a model-serving skill for deploying custom models with versioning, autoscaling, and cost/latency controls.

Goals​

  • Choose serving backends and hardware for your models
  • Configure versioning, traffic splitting, and rollback
  • Add autoscaling, caching, and rate limits to control cost/latency
  • Integrate served models with your agent stack
  • Capture deployment patterns in a reusable serving skill

Lesson Progression​

  • Build the model-serving skill
  • Serving architectures and infrastructure choices
  • Versioning, traffic management, rollback
  • Autoscaling/caching/rate limiting
  • Integration with agents/APIs
  • Capstone: deployed Task API model endpoint; finalize the skill

Outcome & Method​

You finish with a production model endpoint wired to your agents and a reusable serving skill.

Prerequisites​

  • Chapters 63-69 (data through evals)