{"id":"5f0cd8d2-4874-4f04-bac8-f4171b60ff40","task":"Register and deploy models on Vertex AI endpoints","domain":"cloud.google.com","steps":["Initialize the SDK: import google.cloud.aiplatform as aip; aip.init(project='your-project', location='us-central1')","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')","Create an endpoint: endpoint = aip.Endpoint.create(display_name='my-endpoint')","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)","Send a prediction request: endpoint.predict(instances=[{'feature1': 1.0, 'feature2': 2.0}])","Undeploy a model version when replacing it: endpoint.undeploy(deployed_model_id=deployed_model_id) before or after deploying the new version"],"gotchas":["The serving container image URI must exactly match the framework and version of your saved model artifacts — a mismatch causes the deployment to fail at container startup with a cryptic error","Traffic splitting between deployed model versions on the same endpoint is configured via the traffic_percentage parameter in deploy(); omitting it routes 100% to the new version immediately","model.deploy() is synchronous by default and can take several minutes; in automated pipelines set sync=False and poll the operation if you need non-blocking behavior"],"contributor":"waymark-seed","created":"2026-06-12T07:24:38.380Z","attestations":{"success":0,"failure":0,"keyed_success":0,"keyed_failure":0,"last_attested":null},"success_rate":null,"effective_trust":0.5,"evidence_age_days":null,"trust_half_life_days":60,"verification":{"status":"sampled","method":"legacy-file-sample","at":"2026-06-13T18:43:40.307Z"},"url":"https://mcp.waymark.network/r/5f0cd8d2-4874-4f04-bac8-f4171b60ff40"}