Google VertexAI monitoring integration

New Relic's integrations include an integration for reporting your GCP Run data to our products. Here, we explain how to activate the integration and what data it collects.

Activate integration

To enable the integration, follow the standard procedures to connect your GCP service to New Relic.

Configuration and polling

You can change the polling frequency and filter data using configuration options.

Default polling information for the GCP Run integration:

New Relic polling interval: 5 minutes

Find and use data

To find your integration data, go to one.newrelic.com > All capabilities > Infrastructure > GCP and select an integration.

Data is attached to the following event types:

Entity	Event Type	Provider
Endpoint	`GcpVertexAiEndpointSample`	`GcpVertexAiEndpoint`
Feature store	`GcpVertexAiFeaturestoreSample`	`GcpVertexAiFeaturestore`
Feature Online Store	`GcpVertexAiFeatureOnlineStoreSample`	`GcpVertexAiFeatureOnlineStore`
Location	`GcpVertexAiLocationSample`	`GcpVertexAiLocation`
Index	`GcpVertexAiIndexSample`	`GcpVertexAiIndex`
PipelineJob	`GcpVertexAiPipelineJobSample`	`GcpVertexAiPipelineJob`

For more on how to use your data, see Understand and use integration data.

Metric data

This integration collects GCP data for VertexAI.

VertexAI Endpoint data

Metric	Unit	Description
`prediction.online.accelerator.duty_cycle`	Percent	Average fraction of time over the past sample period during which the accelerator(s) were actively processing.
`prediction.online.accelerator.memory.bytes_used`	Bytes	Amount of accelerator memory allocated by the deployed model replica.
`prediction.online.error_count`	Count	Number of online prediction errors.
`prediction.online.memory.bytes_used`	Bytes	Amount of memory allocated by the deployed model replica and currently in use.
`prediction.online.network.received_bytes_count`	Bytes	Number of bytes received over the network by the deployed model replica.
`prediction.online.network.sent_bytes_count`	Bytes	Number of bytes sent over the network by the deployed model replica.
`prediction.online.prediction_count`	Count	Number of online predictions.
`prediction.online.prediction_latencies`	Milliseconds	Online prediction latency of the deployed model.
`prediction.online.private.prediction_latencies`	Milliseconds	Online prediction latency of the private deployed model.
`prediction.online.replicas`	Count	Number of active replicas used by the deployed model.
`prediction.online.response_count`	Count	Number of different online prediction response codes.
`prediction.online.target_replicas`	Count	Target number of active replicas needed for the deployed model.

VertexAI Featurestore data

Metric	Unit	Description
`featurestore.cpu_load`	Percent	The average CPU load for a node in the Featurestore online storage.
`featurestore.cpu_load_hottest_node`	Percent	The CPU load for the hottest node in the Featurestore online storage.
`featurestore.node_count`	Count	The number of nodes for the Featurestore online storage.
`featurestore.online_entities_updated`	Count	Number of entities updated on the Featurestore online storage.
`featurestore.online_serving.latencies`	Milliseconds	Online serving latencies by EntityType.
`featurestore.online_serving.request_bytes_count`	Bytes	Request size by EntityType.
`featurestore.online_serving.request_count`	Count	Featurestore online serving count by EntityType.
`featurestore.online_serving.response_size`	Bytes	Response size by EntityType.
`featurestore.storage.billable_processed_bytes`	Bytes	Number of bytes billed for offline data processed.
`featurestore.storage.stored_bytes`	Bytes	Bytes stored in Featurestore.
`featurestore.streaming_write.offline_processed_count`	Count	Number of streaming write requests processed for offline storage.
`featurestore.streaming_write.offline_write_delays`	Seconds	Time (in second) since the write API is called until it is written to offline storage.

VertexAI FeatureOnlineStore data

Metric	Unit	Description
`featureonlinestore.online_serving.request_count`	Count	Number of serving count by FeatureView.
`featureonlinestore.online_serving.serving_bytes_count`	Bytes	Serving response size by FeatureView.
`featureonlinestore.online_serving.serving_latencies`	Milliseconds	Online serving latencies by FeatureView.
`featureonlinestore.running_sync`	Milliseconds	Number of running syncs at given point of time.
`featureonlinestore.serving_data_ages`	Seconds	Measure of the serving data age in seconds.
`featureonlinestore.serving_data_by_sync_time`	Count	Breakdown of data in Feature Online Store by synced timestamp.
`featureonlinestore.storage.bigtable_cpu_load`	Percent	The average CPU load of nodes in the Feature Online Store.
`featureonlinestore.storage.bigtable_cpu_load_hottest_node`	Percent	The CPU load of the hottest node in the Feature Online Store.
`featureonlinestore.storage.bigtable_nodes`	Count	The number of nodes for the Feature Online Store(Bigtable).
`featureonlinestore.storage.stored_bytes`	Count	Bytes stored in the Feature Online Store.

VertexAI Location data

Metric	Unit	Description
`online_prediction_requests_per_base_model`	Count	Number of requests per base model.
`quota.online_prediction_requests_per_base_model.exceeded`	Count	Number of attempts to exceed the limit on quota metric.
`quota.online_prediction_requests_per_base_model.limit`	Count	Current limit on quota metric.
`quota.online_prediction_requests_per_base_model.usage`	Count	Current usage on quota metric.
`executing_vertexai_pipeline_jobs`	Count	Number of pipeline jobs being executed.
`executing_vertexai_pipeline_tasks`	Count	Number of pipeline tasks being executed.

VertexAI Index data

Metric	Unit	Description
`matching_engine.stream_update.datapoint_count`	Count	Number of successfully upserted or removed datapoints.
`matching_engine.stream_update.latencies`	Milliseconds	The latencies between the user receives a UpsertDatapointsResponse or RemoveDatapointsResponse and that update takes effect.
`matching_engine.stream_update.request_count`	Count	Number of stream update requests.

VertexAI Pipeline Job data

Metric	Unit	Description
`pipelinejob.duration`	Seconds	Runtime seconds of the pipeline job being executed (from creation to end).
`pipelinejob/task_completed_count`	Count	Total number of completed Pipeline Tasks.

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