--- title: Azure Machine Learning through Azure Monitor integration source: https://docs.newrelic.com/docs/infrastructure/microsoft-azure-integrations/azure-integrations-list/azure-machine-learning-monitor-integration --- [New Relic's integrations](https://docs.newrelic.com/docs/infrastructure/introduction-infra-monitoring) include an integration for reporting your [Microsoft Azure Machine Learning](https://azure.microsoft.com/en-in/products/machine-learning/) metrics and other data to New Relic. This document explains how to activate the integration and describes the data reported. ## Features New Relic gathers metrics data from Azure Monitor for the Azure Machine Learning service. Azure Machine Learning is a cloud service for accelerating and managing the machine learning project lifecycle. Machine learning professionals, data scientists, and engineers can use it in their day-to-day workflows to train and deploy models or manage MLOps Using New Relic, you can: - View Azure Machine Learning metrics in pre-built dashboards. - Run [custom queries and visualize the data](https://docs.newrelic.com/docs/infrastructure/integrations-getting-started/getting-started/use-integration-data-new-relic-insights). - Create alert conditions to notify you of changes in data. ## Activate integration [#activate] Follow standard [Azure Monitor integration procedure](https://docs.newrelic.com/docs/infrastructure/microsoft-azure-integrations/azure-integrations-list/azure-monitor) to activate your Azure service in New Relic infrastructure monitoring. ## Configuration and polling [#polling] You can change the polling frequency and filter data using [configuration options](https://docs.newrelic.com/docs/infrastructure/microsoft-azure-integrations/azure-integrations-list/azure-monitor/#settings). New Relic queries your Azure Machine Learning service through the Azure Monitor integration according to a default [polling](https://docs.newrelic.com/docs/infrastructure/microsoft-azure-integrations/azure-integrations-list/azure-monitor/#settings) interval. ## Find and use data [#find-and-use] To [explore your integration data](https://docs.newrelic.com/docs/infrastructure/integrations/find-use-infrastructure-integration-data), go to **[one.newrelic.com/infra](https://one.newrelic.com/infra) > Azure > (select an integration)**. ## Metric data [#metrics] This integration collects the following [metric data](https://docs.newrelic.com/docs/infrastructure/integrations-getting-started/getting-started/understand-integration-data-data-types#metric): ### Azure Machine Learning metrics [#Azure-machine-learning] ### Workspaces [#workspaces] The following table lists the metrics available for the `Microsoft.MachineLearningServices/workspaces` resource type. | Metric | Description | | -------------------------------- | ---------------------------------------------------------------------------------- | | `ActiveCores` | Number of active cores | | `ActiveNodes` | Number of active nodes. These are the nodes which are actively running a job. | | `CancelRequestedRuns` | Number of runs where cancel was requested for this workspace. | | `CancelledRuns` | Number of runs cancelled for this workspace. | | `CompletedRuns` | Number of runs completed successfully for this workspace. | | `CpuCapacityMillicores` | Maximum capacity of a CPU node in millicores. | | `CpuMemoryCapacityMegabytes` | Maximum memory utilization of a CPU node in megabytes. | | `CpuMemoryUtilizationMegabytes` | Memory utilization of a CPU node in megabytes. | | `CpuMemoryUtilizationPercentage` | Memory utilization percentage of a CPU node. | | `CpuUtilization` | Percentage of utilization on a CPU node | | `CpuUtilizationMillicores` | Utilization of a CPU node in millicores | | `CpuUtilizationPercentage` | Utilization percentage of a CPU node. | | `DiskAvailMegabytes` | Available disk space in megabytes. | | `DiskReadMegabytes` | Data read from disk in megabytes | | `DiskUsedMegabytes` | Used disk space in megabytes | | `DiskWriteMegabytes` | Data written into disk in megabytes | | `Errors` | Number of run errors in this workspace | | `FailedRuns` | Number of runs failed for this workspace | | `FinalizingRuns` | Data read from disk in megabytes | | `GpuCapacityMilliGPUs` | Maximum capacity of a GPU device in milli-GPUs | | `GpuEnergyJoules` | Interval energy in Joules on a GPU node | | `GpuMemoryCapacityMegabytes` | Maximum memory capacity of a GPU device in megabytes. | | `GpuMemoryUtilization` | Percentage of memory utilization on a GPU node. | | `GpuMemoryUtilizationMegabytes` | Memory utilization of a GPU device in megabytes | | `GpuMemoryUtilizationPercentage` | Memory utilization percentage of a GPU device | | `GpuUtilization` | Percentage of utilization on a GPU node | | `GpuUtilizationMilliGPUs` | Utilization of a GPU device in milli-GPUs | | `GpuUtilizationPercentage` | Utilization percentage of a GPU device | | `IBReceiveMegabytes` | Network data received over InfiniBand in megabytes | | `IBTransmitMegabytes` | Network data sent over InfiniBand in megabytes | | `IdleCores` | Number of idle cores | | `IdleNodes` | Number of idle nodes | | `LeavingCores` | Number of leaving cores | | `LeavingNodes` | Number of leaving nodes | | `ModelDeployFailed` | Number of model deployments that failed in this workspace | | `ModelDeployStarted` | Number of model deployments started in this workspace | | `ModelDeploySucceeded` | Number of model deployments that succeeded in this workspace | | `ModelRegisterFailed` | Number of model registrations that failed in this workspace | | `ModelRegisterSucceeded` | Number of model registrations that succeeded in this workspace | | `NetworkInputMegabytes` | Network data received in megabytes. Metrics are aggregated in one minute intervals | | `NetworkOutputMegabytes` | Network data sent in megabytes. Metrics are aggregated in one minute intervals. | | `Not Responding Runs` | Number of runs not responding for this workspace. | | `NotStartedRuns` | Number of runs in Not Started state for this workspace | | `PreemptedCores` | Number of preempted cores | | `PreemptedNodes` | Number of preempted nodes | | `PreparingRuns` | Number of runs that are preparing for this workspace. | | `Provisioning Runs` | Number of runs that are provisioning for this workspace. | | `Queued Runs` | Number of runs that are queued for this workspace | | `QuotaUtilizationPercentage` | Percent of quota utilized | | `Started Runs` | Number of runs running for this workspace | | `Starting Runs` | Number of runs started for this workspace | | `StorageAPIFailureCount` | Azure Blob Storage API calls failure count. | | `StorageAPISuccessCount` | Azure Blob Storage API calls success count. | | `TotalCores` | Number of total cores | | `TotalNodes` | Number of total nodes | | `UnusableCores` | Number of unusable cores | | `UnusableNodes` | Number of unusable nodes | | `Warnings` | Number of run warnings in this workspace | The following table lists the metrics available for the `Microsoft.MachineLearningServices/workspaces/onlineEndpoints/deployments` resource type. | Metric | Description | | -------------------------------- | ---------------------------------------------------------------- | | `CpuMemoryUtilizationPercentage` | Percentage of memory utilization on an instance | | `CpuUtilizationPercentage` | Percentage of CPU utilization on an instance | | `DataCollectionErrorsPerMinute` | The number of data collection events dropped per minute | | `DataCollectionEventsPerMinute` | The number of data collection events processed per minute. | | `DeploymentCapacity` | The number of instances in the deployment | | `DiskUtilization` | Percentage of disk utilization on an instance | | `GpuEnergyJoules` | Interval energy in Joules on a GPU node | | `GpuMemoryUtilizationPercentage` | Percentage of GPU memory utilization on an instance | | `GpuUtilizationPercentage` | Percentage of GPU utilization on an instance. | | `RequestLatency_P50` | The average P50 request latency | | `RequestLatency_P90` | The average P90 request latency | | `RequestLatency_P95` | The average P95 request latency | | `RequestLatency_P99` | The average P99 request latency | | `RequestsPerMinute` | The number of requests sent to online deployment within a minute | The following table lists the metrics available for the `Microsoft.MachineLearningServices/workspaces/onlineEndpoints` resource type. | Metric | Description | | ------------------------------- | ---------------------------------------------------------------------------------------------------------------- | | `ConnectionsActive` | The total number of concurrent TCP connections active from clients | | `DataCollectionErrorsPerMinute` | The number of data collection events dropped per minute | | `DataCollectionEventsPerMinute` | The number of data collection events processed per minute | | `NetworkBytes` | The bytes per second served for the endpoint | | `NewConnectionsPerSecond` | The average number of new TCP connections per second established from clients | | `RequestLatency` | The average complete interval of time taken for a request to be responded in milliseconds | | `RequestLatency_P50` | The average P50 request latency aggregated by all request latency values collected over the selected time period | | `RequestLatency_P90` | The average P90 request latency aggregated by all request latency values collected over the selected time period | | `RequestLatency_P95` | The average P95 request latency aggregated by all request latency values collected over the selected time period | | `RequestLatency_P99` | The average P99 request latency aggregated by all request latency values collected over the selected time period | | `RequestsPerMinute` | The number of requests sent to online endpoint within a minute |