The Azure Machine Learning service empowers developers and data scientists with a wide range of productive experiences for building, training, and deploying machine learning models faster. Use Datadog to monitor your Azure Machine Learning performance and utilization in context with the rest of your applications and infrastructure.
Get metrics from Azure Machine Learning to:
If you haven’t already, set up the Microsoft Azure integration first. There are no other installation steps.
| azure.machinelearningservices_workspaces.completed_runs (gauge) | The number of runs completed successfully for this workspace. Shown as operation |
| azure.machinelearningservices_workspaces.started_runs (gauge) | The number of runs started for this workspace. Shown as operation |
| azure.machinelearningservices_workspaces.failed_runs (gauge) | The number of runs failed for this workspace. Shown as operation |
| azure.machinelearningservices_workspaces.model_register_succeeded (gauge) | The number of model registrations that succeeded in this workspace. |
| azure.machinelearningservices_workspaces.model_register_failed (gauge) | The number of model registrations that failed in this workspace. |
| azure.machinelearningservices_workspaces.model_deploy_started (gauge) | The number of model deployments started in this workspace. |
| azure.machinelearningservices_workspaces.model_deploy_succeeded (gauge) | The number of model deployments that succeeded in this workspace. |
| azure.machinelearningservices_workspaces.moddel_deploy_failed (gauge) | The number of model deployments that failed in this workspace. |
| azure.machinelearningservices_workspaces.total_nodes (gauge) | The number of total nodes. This total includes some of Active Nodes, Idle Nodes, Unusable Nodes, Premepted Nodes, Leaving Nodes. Shown as node |
| azure.machinelearningservices_workspaces.active_nodes (gauge) | The number of Acitve nodes. These are the nodes which are actively running a job. Shown as node |
| azure.machinelearningservices_workspaces.idle_nodes (gauge) | The number of idle nodes. Idle nodes are the nodes which are not running any jobs but can accept new job if available. Shown as node |
| azure.machinelearningservices_workspaces.unusable_nodes (gauge) | The number of unusable nodes. Unusable nodes are not functional due to some unresolvable issue. Azure will recycle these nodes. Shown as node |
| azure.machinelearningservices_workspaces.preempted_nodes (gauge) | The number of preempted nodes. These nodes are the low priority nodes which are taken away from the available node pool. Shown as node |
| azure.machinelearningservices_workspaces.leaving_nodes (gauge) | The number of leaving nodes. Leaving nodes are the nodes which just finished processing a job and will go to Idle state. Shown as node |
| azure.machinelearningservices_workspaces.total_cores (gauge) | The number of total cores. Shown as core |
| azure.machinelearningservices_workspaces.active_cores (gauge) | The number of active cores. Shown as core |
| azure.machinelearningservices_workspaces.idle_cores (gauge) | The number of idle cores. Shown as core |
| azure.machinelearningservices_workspaces.unusable_cores (gauge) | The number of unusable cores. Shown as core |
| azure.machinelearningservices_workspaces.preempted_cores (gauge) | The number of preempted cores. Shown as core |
| azure.machinelearningservices_workspaces.leaving_cores (gauge) | The number of leaving cores. Shown as core |
| azure.machinelearningservices_workspaces.quota_utilization_percentage (gauge) | The percent of quota utilized. Shown as percent |
| azure.machinelearningservices_workspaces.cpuutilization (gauge) | CPU utilization Shown as percent |
| azure.machinelearningservices_workspaces.gpuutilization (gauge) | GPU utilization Shown as percent |
The Azure Machine Learning integration does not include any events.
The Azure Machine Learning integration does not include any service checks.
Need help? Contact Datadog support.
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