Kubernetes has transformed container orchestration, but managing Kubernetes clusters and Internal Developer Platforms (IDPs) at scale remains a complex challenge for platform engineers. While ClusterAPI offers a declarative way to provision and operate clusters, it often brings complexity through verbose YAML and integration difficulties.

This talk introduces an AIOps-driven approach that leverages ClusterAPI, Sveltos, and templated automation to enable end-to-end lifecycle management—provisioning, upgrades, and teardown—across diverse environments.

Through a real-world, large-scale platform demo, we’ll explore intelligent multi-cluster management, with enhanced observability, automated scaling, and proactive anomaly detection. Attendees will gain practical insights into building adaptive, resilient, and developer-friendly platform experiences on Kubernetes.

This talk explores how platform engineering teams can build a self-service AI/ML infrastructure on Kubernetes—enabling dynamic provisioning, policy enforcement, and full observability—while allowing data scientists to stay focused on model development.

While ClusterAPI simplifies cluster provisioning, managing AI/ML workloads demands a full-lifecycle approach. We demonstrate how to extend ClusterAPI with tools like Backstage, OpenFeature, k0s, Prometheus, Sveltos, and k0rdent to build a scalable, secure, and automated AI/ML platform.

Key takeaways include:

• AI/ML cluster provisioning with ClusterAPI and k0s
• Policy-driven multi-cluster automation
• Controlled model rollouts with feature flags
• Self-service ML environments via an Internal Developer Platform
• Observability and performance monitoring at scale

Join me to discover how a Kubernetes-native approach can empower your AI/ML platform engineering journey.