In today's hybrid, multi-cloud world, capacity planning is no longer a static spreadsheet exercise-it's a dynamic, data-driven discipline that demands automation, intelligence, and scale. Capacity Engineering with Python and AI is a practical, hands-on guide for infrastructure engineers, SREs, and cloud architects who want to build modern capacity management systems that go beyond traditional monitoring.

This book shows you how to design and implement end-to-end capacity engineering platforms using Python, statistical forecasting, and AI-powered reasoning. You'll learn how to collect and normalize telemetry across on-prem and cloud environments, build accurate forecasting models, detect anomalies before they become outages, and transform raw data into actionable insights.

Going further, the book introduces AI-augmented analysis using large language models to explain trends, prioritize risks, and recommend safe, auditable remediation actions-without sacrificing control or reliability.

Through real-world examples spanning AWS, Azure, Google Cloud, Kubernetes, and on-prem systems, you will build a complete pipeline: from metrics collection to automated, human-approved infrastructure changes using Terraform and Ansible.

Inside, you'll learn how to:

• Design a scalable capacity engineering architecture across hybrid environments
• Build resilient data collection and normalization pipelines in Python
• Apply statistical and machine learning techniques for capacity forecasting
• Detect anomalies and reduce alert fatigue with intelligent systems
• Use AI as a reasoning layer to generate insights and recommendations
• Implement safe, closed-loop remediation workflows with human oversight
• Integrate capacity engineering into CI/CD, FinOps, and governance frameworks

Whether you're managing a single cluster or a global multi-cloud platform, this book equips you with the tools and patterns to turn capacity planning into a proactive, intelligent system.