GCP Vertex AI

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| | |
| --- | --- |
| Version | 0.3.1 [beta] (View all) |
| Compatible Kibana version(s) | 8.17.0 or higher |
| Supported Serverless project types
What’s this? | Security
Observability |
| Subscription level
What’s this? | Basic |
| Level of support
What’s this? | Elastic |

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Vertex AI is a platform that enables the training and deployment of machine learning models and AI applications. It aims to streamline and expedite the development and deployment process for ML models, offering a variety of features and integrations tailored for enterprise-level workflows.

The integration with Google Cloud Platform (GCP) Vertex AI allows you to gather metrics such as token usage, latency, overall invocations, and error rates for deployed models. Additionally, it tracks resource utilization metrics for the model replicas as well as prediction metrics of endpoints.

The GCP Vertex AI includes Vertex AI Model Garden Publisher Model metrics under the publisher category and the Vertex AI Endpoint metrics under the prediction category.

You need Elasticsearch for storing and searching your data and Kibana for visualizing and managing it. You can use our hosted Elasticsearch Service on Elastic Cloud, which is recommended, or self-manage the Elastic Stack on your own hardware.

Before using any GCP integration you will need:

• GCP Credentials to connect with your GCP account.
• GCP Permissions to make sure the service account you’re using to connect has permission to share the relevant data.

There isn’t a single, specific role required to view metrics for Vertex AI. Access depends on how the models are deployed and the permissions granted to your Google Cloud project and user account.

However, to summarize the necessary permissions and implied roles, you’ll generally need a role that includes the following permissions:

• monitoring.metricDescriptor.list: Allows you to list available metric descriptors.
• monitoring.timeSeries.list: Allows you to list time series data for the metrics.

These permissions are included in many roles, but here are some of the most common ones:

• roles/monitoring.viewer: This role provides read-only access to Cloud Monitoring metrics.
• roles/aiplatform.user: This role grants broader access to Vertex AI, including model viewing and potentially metric access.
• More granular roles: For fine-grained control (recommended for security best practices), consider using a custom role built with the specific permissions needed. This would only include the necessary permissions to view model metrics, rather than broader access to all Vertex AI or Cloud Monitoring resources. This requires expertise in IAM (Identity and Access Management).
• Predefined roles with broader access: These roles provide extensive permissions within the Google Cloud project, giving access to metrics but granting much broader abilities than necessary for just viewing metrics. These are generally too permissive unless necessary for other tasks. Examples are roles/aiplatform.user or roles/editor.

Vertex AI offers two primary deployment types,

• Provisioned Throughput: Suitable for high-usage applications with predictable workloads and a premium on guaranteed performance.
• Pay-as-you-go: Ideal for low-usage applications, batch processing, and applications with unpredictable traffic patterns.

Now, you can track and monitor different deployment types (provisioned throughput and pay-as-you-go) in Vertex AI using the Model Garden Publisher resource.

To fetch the metrics, enter the project_id and the credentials file/json.

Refer to Google Cloud Platform configuration for more information about the configuration.

Refer to Google Cloud Platform troubleshooting for more information about troubleshooting the issue.

{
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        "provider": "gcp",
        "account": {
            "name": "elastic-sa",
            "id": "elastic-sa"
        }
    },
    "agent": {
        "name": "docker-fleet-agent",
        "id": "f9c4beb9-c0c0-47ca-963a-a9dc00e2df5e",
        "ephemeral_id": "6c42a949-d522-44bf-818b-12c4a5908b90",
        "type": "metricbeat",
        "version": "8.15.2"
    },
    "@timestamp": "2024-11-07T05:50:40.000Z",
    "ecs": {
        "version": "8.0.0"
    },
    "gcp": {
        "vertexai": {
            "publisher": {
                "online_serving": {
                    "token_count": 13
                }
            }
        },
        "labels": {
            "resource": {
                "model_user_id": "gemini-1.5-flash-002",
                "model_version_id": "",
                "publisher": "google",
                "location": "us-central1"
            },
            "metrics": {
                "request_type": "shared",
                "type": "input"
            }
        }
    },
    "service": {
        "type": "gcp"
    },
    "data_stream": {
        "namespace": "default",
        "type": "metrics",
        "dataset": "gcp_vertexai.metrics"
    },
    "elastic_agent": {
        "id": "f9c4beb9-c0c0-47ca-963a-a9dc00e2df5e",
        "version": "8.15.2",
        "snapshot": false
    },
    "host": {
        "hostname": "docker-fleet-agent",
        "ip": [
            "172.25.0.7"
        ]
    },
    "metricset": {
        "period": 60000,
        "name": "metrics"
    },
    "event": {
        "duration": 913154084,
        "agent_id_status": "verified",
        "ingested": "2024-11-07T05:57:17Z",
        "module": "gcp",
        "dataset": "gcp_vertexai.metrics"
    }
}

ECS Field Reference

Please refer to the following document for detailed information on ECS fields.