| name | vertex-ai |
| description | Build and deploy ML models and generative AI applications on Vertex AI. Covers Model Garden, Gemini API, custom training, endpoint deployment, Agent Builder, and IAM. Warns before deploying endpoints (billable). Integrates with Vertex AI MCP server. |
| version | 0.1 |
| triggers | ["vertex AI","Gemini on GCP","model garden","deploy ML model","vertex endpoint","generative AI GCP","agent builder","vertex training","PaLM","Gemini API","vertex AI pipeline"] |
| required_scopes | ["aiplatform.endpoints.create","aiplatform.endpoints.get","aiplatform.endpoints.predict","aiplatform.models.get","aiplatform.models.list","aiplatform.trainingPipelines.create","aiplatform.trainingPipelines.get"] |
| mcp_servers | ["google-vertex-ai"] |
Vertex AI
Unified ML platform for training, deploying, and serving models — including Gemini, generative AI, and custom models.
Naming currency: the platform was renamed to the Gemini Enterprise Agent Platform (GEAP) at Next '26 (2026-04-22). The rebrand is branding-only — vertex-ai persists in gcloud groups, SDK imports, API hostnames, Terraform resources, and doc URLs, so this skill keeps them. For agentic system design (ADK, Agent Runtime, multi-agent, MCP/A2A/AP2, agent evaluation), route to agent-architect; this skill covers platform/model mechanics.
Key Products
| Product | Use |
|---|
| Gemini API on Vertex | Production-grade Gemini access with data governance |
| Model Garden | Browse and deploy 150+ models (Gemini, Llama, etc.) |
| Custom Training | Train on managed infrastructure (GPUs/TPUs) |
| Vertex AI Endpoints | Serve predictions from deployed models |
| Agent Builder | Build conversational agents on Vertex |
| Vertex AI Pipelines | Orchestrate ML workflows (Kubeflow Pipelines) |
Core Patterns
Call Gemini via Vertex (Python)
import vertexai
from vertexai.generative_models import GenerativeModel
vertexai.init(project="PROJECT_ID", location="us-central1")
model = GenerativeModel("gemini-2.0-flash-001")
response = model.generate_content("Explain Cloud Run in one sentence.")
print(response.text)
Deploy an endpoint (billable — confirm first)
gcloud ai endpoints create \
--display-name=ENDPOINT_NAME \
--region=REGION \
--project=PROJECT_ID
Grant Vertex AI access (least-privilege)
gcloud projects add-iam-policy-binding PROJECT_ID \
--member="serviceAccount:SA@PROJECT.iam.gserviceaccount.com" \
--role="roles/aiplatform.user"
Safety Rules
- Endpoints incur cost even when idle — undeploy when not needed
- Use
roles/aiplatform.user for inference; roles/aiplatform.admin only for platform admins
- Cost warning: GPUs and TPUs are expensive — confirm instance type and count before training
References
