Building Self-Healing Cloud Infrastructure for Resilient AI Platforms
About this session
AI-driven applications and cloud platforms must remain available even when services fail, workloads become unstable, or dependencies behave unpredictably. This session presents four practical practices for building self-healing cloud infrastructure: automated failure detection, automated recovery, blast-radius reduction, and operational resilience.
The presentation explores how infrastructure teams can identify failures early, automatically initiate recovery mechanisms, and reduce dependence on manual intervention. Attendees will learn how automated detection and recovery help cloud platforms respond consistently to service disruptions and restore affected components more efficiently.
The session also examines techniques for limiting the blast radius of infrastructure failures. By isolating affected services, workloads, or dependencies, engineering teams can prevent a localized issue from developing into a broader platform disruption. This approach improves system stability and supports predictable operations across distributed and AI-enabled environments.
The presentation explains how these practices strengthen resilience across cloud-native, DevOps, Site Reliability Engineering, AIOps, AI infrastructure, and automation environments. Attendees will leave with a practical framework for designing infrastructure that detects problems, recovers automatically, contains failures, and continues operating with minimal disruption.
This session is intended for cloud engineers, DevOps professionals, SREs, platform engineers, AI infrastructure practitioners, architects, and technical leaders building autonomous and resilient cloud systems.
Speaker
Key takeaways
- Learn how automated failure detection and recovery can reduce manual intervention and restore affected cloud components more consistently. Understand how blast-radius reduction and fault isolation can prevent localized failures from escalating across distributed AI platforms.