AI Workload Orchestration is the automated coordination of computing resources, data pipelines, models, and AI tasks to execute workloads efficiently. It determines where and when tasks should run, allocates resources such as GPUs and CPUs, manages dependencies, and can adjust workloads as demand or system conditions change.
In large AI environments, orchestration helps coordinate activities such as data preparation, model training, batch processing, and inference across multiple machines or cloud resources. It can prioritize jobs, scale infrastructure, recover failed tasks, and prevent resource conflicts, making complex AI pipelines more reliable and efficient.
