"KServe on Kubernetes: Production Model Serving with Canary Releases and Autoscaling"
Modern ML systems fail in production for reasons that rarely appear in notebooks: traffic spikes, cold starts, bad rollout decisions, routing ambiguity, and invisible regressions between model revisions. This book is written for experienced platform engineers, MLOps practitioners, and senior Kubernetes users who need to run KServe as a reliable production serving layer, not merely deploy a demo model. It assumes readers want operational clarity, precise trade-offs, and infrastructure-level control.
Across the book, readers build a deep understanding of KServe’s architecture, the InferenceService contract, deployment modes, Knative integration, and Standard versus ModelMesh operating models. The coverage then moves into autoscaling for inference workloads, including KPA versus HPA, concurrency tuning, scale-to-zero, cold-start management, and resource-aware scheduling. From there, the book develops safe progressive delivery practices through traffic management, canary rollout mechanics, revision-aware observability, promotion gates, rollback strategy, and production troubleshooting under real load.
A distinguishing strength of this book is its focus on decision-making in live systems: not just how KServe works, but when to choose one mode, routing layer, scaling policy, or release strategy over another. It is structured for advanced readers who are already comfortable with Kubernetes fundamentals and want a rigorous, implementation-minded guide to serving models safely at scale.
