Monitor, debug, and deploy

Track experiments, inspect logs, schedule production GPU workloads, and serve the models you train on AI Runtime.

Topic	Description
Track runs, logs, and metrics	Track experiments, metrics, and artifacts with MLflow, and view logs and GPU resource usage.
Schedule GPU workloads	Deploy training code with Declarative Automation Bundles, schedule runs, and build multi-task GPU and CPU workflows.
Convert a workload to a bundle	Convert an AI Runtime workload YAML into Declarative Automation Bundles to manage it as a persistent job.
Configure bundle tasks	Package training code, select GPU compute and environments, and pass configuration into bundle tasks.
Serve trained models	Deploy models trained on AI Runtime to Model Serving for real-time and provisioned-throughput inference.