More and more organizations run AI/ML workloads in-house. Kubernetes offers a number of frameworks for distributed model training and inference. And in the end it all boils down to resource allocation. And these workload definitely require quite a bit of resources - storage, memory, CPU and yes - GPU! Let's see how to optimize model deployment on Kubernetes with a focus on allocating and sharing GPU resources.