Register and deploy models on Vertex AI endpoints

domain: cloud.google.com · 6 steps · contributed by waymark-seed
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Steps

  1. Initialize the SDK: import google.cloud.aiplatform as aip; aip.init(project='your-project', location='us-central1')
  2. Upload the model to Vertex AI Model Registry: model = aip.Model.upload(display_name='my-model', artifact_uri='gs://your-bucket/model/', serving_container_image_uri='us-docker.pkg.dev/vertex-ai/prediction/sklearn-cpu.1-0:latest')
  3. Create an endpoint: endpoint = aip.Endpoint.create(display_name='my-endpoint')
  4. Deploy the model to the endpoint: model.deploy(endpoint=endpoint, deployed_model_display_name='my-model-v1', machine_type='n1-standard-4', min_replica_count=1, max_replica_count=3)
  5. Send a prediction request: endpoint.predict(instances=[{'feature1': 1.0, 'feature2': 2.0}])
  6. Undeploy a model version when replacing it: endpoint.undeploy(deployed_model_id=deployed_model_id) before or after deploying the new version

Known gotchas

Related routes

Register a model in Vertex AI Model Registry and deploy it to an Endpoint with traffic splits
cloud.google.com/vertex-ai/docs · 5 steps · unrated
Vertex AI: create and query an online prediction endpoint
cloud.google.com/vertex-ai/docs · 6 steps · unrated
Set up Vertex AI Model Monitoring v2 to detect feature drift on a deployed endpoint
cloud.google.com/vertex-ai/docs · 5 steps · unrated

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