Vertex ai

Skill jpantsjoha/googlecloud-plugin/skills/vertex-ai

Build and deploy ML models and generative AI workloads — models, prompts, tuning and endpoints — on Vertex AI. Covers Model Garden, the Gemini API, custom training, endpoint deployment, and IAM. Warns before deploying endpoints (billable). Integrates with Vertex AI MCP server. Use when the user mentions: Gemini on GCP, vertex endpoint, generative AI GCP, vertex training, PaLM, vertex AI pipeline. Use this for models, training and endpoints; use agent-architect instead for anything agentic — Agent Builder, ADK, Agent Runtime, A2A, AP2 and multi-agent topologies all belong there, not here.From its SKILL.md

Install

npx -y skills add jpantsjoha/googlecloud-plugin --skill vertex-ai

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SKILL.md

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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)

# Billable: endpoints charge per node-hour when deployed
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:[email protected]" \
  --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