Green Software Engineering for AI Systems

Green Software Engineering for AI Systems addresses one of the most urgent yet overlooked challenges in modern computing: the environmental impact of software and AI at scale. As cloud-native platforms and AI workloads grow rapidly, inefficient software design and operational choices are driving excessive energy consumption and carbon emissions-often unintentionally. This book reframes sustainability from a policy or infrastructure concern into a core engineering responsibility.

The book takes a practical, end-to-end approach across the full software lifecycle. It begins by explaining the carbon cost of modern software and AI systems and introduces foundational principles such as energy proportionality, carbon efficiency, and sustainability as a non-functional requirement. From there, it explores architectural decisions-monoliths versus microservices, event-driven systems, data locality, and storage efficiency-that have the greatest impact on sustainability. Dedicated chapters focus on designing efficient AI pipelines, balancing training and inference costs, and optimizing model size, serving, and scaling. Beyond design, the book dives into development practices, tooling, and CI/CD pipelines, showing how teams can measure, monitor, and continuously improve sustainability using meaningful metrics, KPIs, and dashboards. It also covers operating green systems in production through carbon-aware autoscaling, observability, governance models, and GreenOps practices, all grounded in real-world experience.

In conclusion, this book equips software engineers, AI practitioners, and technical leaders with actionable techniques to build high-performing, reliable, and sustainable AI-driven systems-helping organizations scale responsibly in a cloud-first world.

What you will learn:

• Design software and AI architectures optimized for energy efficiency and lower carbon impact
• Apply practical sustainability metrics to engineering and operational decisions
• Build green CI/CD pipelines with carbon-aware deployment strategies
• Balance performance, reliability, cost, and environmental impact in AI systems
• Embed sustainability into engineering governance and operational workflows

Who this book is for:

This book is for software engineers, architects, SREs, and engineering managers working on cloud-native and AI-driven systems. Readers should have a basic understanding of software development and distributed systems