Running AI Workloads on Kubernetes: What Changes and What Breaks

Kubernetes is widely used to host AI training, inference, and batch workloads, but production practices often lag adoption because its default scheduling, autoscaling, and monitoring capabilities were designed primarily for stateless services and conventional batch processing. AI training requires coordinated gang scheduling, dedicated GPU allocation, workload priorities, and hardware-aware placement to avoid partially scheduled jobs that reserve idle resources, while inference requires responsive scaling that accounts for model-loading delays and batch workloads generally fit conventional autoscaling patterns. The discussion emphasizes that GPU costs extend beyond compute to include data infrastructure, networking, observability, retraining, and operational resilience, and argues that effective monitoring must combine infrastructure health, pipeline execution, and model-performance signals such as drift. It also highlights visibility gaps between Kubernetes and Spark metrics, including executor failures, driver heartbeat delays, pending pods, shuffle bottlenecks, and memory overhead. The vendor promotes Acceldata xLake as a Kubernetes-based unified control plane for scheduling, orchestration, conflict detection, and per-job observability across Spark, Trino, JupyterHub, and Python workloads, while noting that its performance and reliability figures are vendor-reported.

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