Editor’s Note: The following is an article written for and published in DZone’s 2025 Trend Report, Kubernetes in the Enterprise: Optimizing the Scale, Speed, and Intelligence of Cloud Operations.

After training a machine learning model, the inference phase must be fast, reliable, and cost efficient in production. Serving inference at scale, however, brings difficult problems: GPU/resource management, latency and batching, model/version rollout, observability, and orchestration of ancillary services (preprocessors, feature stores, and vector databases). Running artificial intelligence and machine learning (AI/ML) on Kubernetes gives us a scalable, portable platform for training and serving models. Kubernetes schedules GPUs and other resources so that we can pack workloads efficiently and autoscale to match traffic for both batch jobs and real-time inference. It also coordinates multi-component stacks — like model servers, preprocessors, vector DBs, and feature stores — so that complex pipelines and low-latency endpoints run reliably.