AI Infrastructure Field Guide

Your next AI deployment needs more than a model. It needs a plan.

Which tools belong in your stack? How will you monitor performance, control GPU costs, manage releases, and recover when something breaks?

AI Infrastructure Field Guide: 10 Open-Source Projects for DevOps Engineers helps you answer those questions with clear explanations, colourful diagrams, practical checklists, and focused experiments.

Explore Argo CD, Kubeflow, MLflow, Prometheus, Grafana, vLLM, NVIDIA Triton, NVIDIA Dynamo, OpenTelemetry Collector, and KEDA-and understand where each fits.

Inside, you'll discover how to:

• Choose tools with purpose: connect each project to a real operational problem.

• Understand model serving: explore capacity, latency, hardware compatibility, and scaling.

• Make performance visible: identify useful metrics, dashboards, and telemetry.

• Plan safer releases: think through validation, rollback, and recovery.

• Keep costs and risks in view: consider idle compute, access controls, and sensitive data.

• Turn learning into action: follow a 30-day plan and use worksheets to review your first experiment.

Written for DevOps engineers, platform engineers, SREs, and technical learners moving into MLOps and LLM operations, this concise guide provides a practical introduction to an increasingly complex landscape.

Start with one problem. Choose one tool. Build the confidence to explain why it belongs in your stack.