Shipping AI: From Prototype to Production Systems
Published by Self-published · Tech
Covers the full lifecycle of taking AI systems from prototype to production, including deployment, scaling, and monitoring.
Who this book is for
AI and ML engineers, platform engineers, engineering managers, DevOps and SRE teams, and data engineers who build or run AI systems in production. It assumes software engineering fundamentals but no machine learning research background.
Table of contents
- The Production Gap
- AI System Architecture
- Inference Infrastructure
- Latency Budgets and Performance
- Prompt and Model Versioning
- Evaluation Pipelines
- Monitoring and Observability
- Cost Management
- Agent Orchestration in Production
- Rollback and Incident Response
- Safety and Guardrails in Production
- Case Studies and Reference Architectures
Excerpt
Every AI demo lies. Not intentionally, but structurally. A prototype that answers questions correctly on a developer's laptop tells you nothing about how it will perform when 500 users hit it simultaneously, when the model returns a hallucinated answer to a customer's billing question, or when your monthly inference bill arrives at six figures. The gap between a working AI prototype and a production AI system is wider than most engineers expect. This chapter explains why that gap exists, what falls into it, and how to assess whether your system is ready to cross it.
