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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. |