Understanding AI workloads, scheduling fundamentals, and the challenges is
necessary but not sufficient—organizations need to know what tools exist and
how to apply them. This paper catalogs the solutions available for AI workload
scheduling: Kubernetes native features, scheduler extensions, batch schedulers,
ML platform tools, and workflow orchestrators. It provides a reference table
mapping challenges to solutions, practical guidance on choosing a scheduling
stack, common patterns and anti-patterns, and concrete use cases showing how
organizations configure scheduling for different scenarios.