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The allure of AI automating complex infrastructure management on AWS, Azure, and GCP is strong. Promises of self-healing systems, predictive scaling, and optimized resource utilization abound. However, the core principles of Site Reliability Engineering – prioritizing stability, reliability, and predictability – clash with the "black box" nature and potential unpredictability of AI. This session dives into the SRE perspective, exploring the inherent risks of letting current AI models directly manage production Kubernetes clusters and cloud resources. We'll outline the non-negotiable safeguards, controls, and observability required before SREs can cautiously embrace AI, moving from hype to hardened reality. Learn practical approaches like GitOps integration, policy enforcement, human-in-the-loop validation, and robust monitoring needed to bridge the gap between AI's potential and production safety.
