AI/ML Workloads on Kubernetes: Simplifying GPU Cluster Management

Overview

AI and ML workloads are pushing Kubernetes to its limits. GPU node pools, resource quotas, job scheduling, and cost management add layers of complexity that most teams aren't prepared for.

Join Ben Ghazi for a practical session on managing AI/ML infrastructure on Kubernetes with Codiac. We'll cover how to provision GPU clusters, deploy training jobs, manage resource quotas, and keep costs under control—all without drowning in YAML or custom operators.

Whether you're running your first ML training pipeline or scaling a production inference fleet, this webinar will give you actionable patterns for simplifying GPU cluster management.