You manage a team of data scientists who use a cloud-based backend system to submit training jobs. This system has become very difficult to administer, and you want to use a managed service instead…

Vertex AI Training is a fully managed service that allows users to submit custom training jobs using virtually any ML framework, including Keras, PyTorch, scikit-learn, and custom libraries, typically via custom containers. This directly addresses the need for a managed solution that supports diverse frameworks without the administrative overhead of self-managed infrastructure.

While Kubeflow on Google Kubernetes Engine (GKE) provides powerful MLOps capabilities, it requires significant administrative effort to set up and maintain the GKE cluster and Kubeflow itself, contradicting the goal of moving away from a "difficult to administer" system.

Creating and managing a library of VM images on Compute Engine for different frameworks is a manual, unmanaged approach that increases administrative burden rather than reducing it, and does not offer a unified, managed job submission system.

Setting up Slurm workload manager introduces another layer of infrastructure management and administration, which goes against the requirement of transitioning to a managed service to simplify operations.