Chaos Engineering for AI Infrastructure Guides Resilience

AI reliability cannot stop at uptime when degraded retrieval, latency, or model quality can quietly break user trust. This book addresses the operational gap between conventional Kubernetes chaos testing and AI-specific failure modes.

• –Covers GPU failures, serving degradation, RAG dependency outages, and agentic workflow breakdowns
• –Treats resilience as an end-to-end property spanning compute, data, models, and orchestration
• –Kubernetes focus makes the material relevant to platform and MLOps teams already running containerized AI workloads
• –The strongest value is its emphasis on deliberate failure injection before production incidents expose hidden dependencies
• –Readers should pair the guidance with observability, SLOs, fallback strategies, and automated recovery testing