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VertexAI

VertexAI

BETA
Feature List
✓ ML Store
✓ ML Features
✓ Hyper parameters
In this section, we provide guides and references to use the VertexAI connector. Configure and schedule VertexAI metadata from the OpenMetadata UI:

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How to Run the Connector Externally

To run the Ingestion via the UI you’ll need to use the OpenMetadata Ingestion Container, which comes shipped with custom Airflow plugins to handle the workflow deployment. If, instead, you want to manage your workflows externally on your preferred orchestrator, you can check the following docs to run the Ingestion Framework anywhere.

External Schedulers

Get more information about running the Ingestion Framework Externally

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Requirements

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Python Requirements

We have support for Python versions 3.9-3.11
To run the VertexAI ingestion, you will need to install: 
pip3 install "openmetadata-ingestion[vertexai]"

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GCP Permissions

To execute metadata extraction workflow successfully the user or the service account should have enough access to fetch required data. Following table describes the minimum required permissions
#GCP PermissionRequired For
1aiplatform.models.getMetadata Ingestion
2aiplatform.models.listMetadata Ingestion

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Metadata Ingestion

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1. Define the YAML Config

This is a sample config for VertexAI:

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2. Run with the CLI

First, we will need to save the YAML file. Afterward, and with all requirements installed, we can run: 
metadata ingest -c <path-to-yaml>
Note that from connector to connector, this recipe will always be the same. By updating the YAML configuration, you will be able to extract metadata from different sources.