There is a moment every DevOps engineer experiences when they first look under the hood of an AI system in production. The monitoring dashboard shows green across every infrastructure metric. CPU utilization is normal, memory is stable, error rates are flat. Yet the model is quietly producing bad predictions, and nobody can explain why. That moment captures the central challenge of this book: traditional DevOps practices, built for deterministic software systems, break down when applied to AI workloads without significant adaptation.
The software industry has spent the last two decades perfecting the art of shipping reliable code. Then AI arrived and quietly upended those rules. A model can return a perfectly formatted HTTP 200 response that contains a completely wrong answer, and neither your monitoring dashboard nor your pager will ever know. The error is semantic rather than technical, and our traditional tooling has no vocabulary for semantic errors. The DevOps engineer who masters AI operations will find no shortage of demand for their skills.
Inside, you'll discover:
• The infrastructure fundamentals that make GPU workloads different from CPU
• How to build CI/CD pipelines designed for model artifacts
• Model serving strategies including canary, shadow, and blue-green deployments
• How to monitor AI in production across four observability layers
• Detecting and responding to data drift, concept drift, and prediction drift
• LLMOps for generative AI including prompt management and guardrails
• Cost optimization and GPU FinOps to reduce infrastructure costs
This book is written for the DevOps engineer who wants to bridge that gap. It assumes you already understand containers, Kubernetes, CI/CD pipelines, and cloud infrastructure. You do not need to be a data scientist. You need to be an engineer who understands the unique operational demands of AI systems and knows how to apply DevOps discipline to meet them.
