Agent skill

vertex-ai-gemini

Google Cloud Vertex AI for enterprise Gemini deployments — production scaling, fine-tuning, and MLOps. Use when deploying Gemini in GCP-native environments, running fine-tuning jobs, needing enterprise IAM controls, VPC isolation, batch prediction at scale, or production ML pipelines on Google Cloud.

View SKILL.md on GitHub Repository
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Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/other/vertex-ai-gemini

Metadata

Additional technical details for this skill

tags
vertex-ai google-cloud gemini enterprise mlops
agents 
[
    "claude-code",
    "openai-codex",
    "gemini-cli",
    "cursor"
]
author
terminal-skills
version
1.0.0
category
data-ai
use cases 
[
    "Deploy Gemini with enterprise IAM, VPC controls, and compliance requirements",
    "Fine-tune Gemini on proprietary data for domain-specific use cases",
    "Run batch predictions on millions of records with managed infrastructure"
]

SKILL.md

Vertex AI — Gemini on Google Cloud

Overview

Vertex AI is Google Cloud's enterprise ML platform. It provides access to the same Gemini models as Google AI Studio, but with enterprise-grade features: IAM-based auth (no API keys), VPC Service Controls for data isolation, audit logging, fine-tuning capabilities, batch prediction jobs, and integration with GCP data services like BigQuery and Cloud Storage.

Vertex AI vs Google AI Studio

Feature Google AI Studio Vertex AI
Auth API Key Service Account / IAM
Data residency Limited GCP regions
VPC isolation ❌ ✅
Audit logging ❌ ✅ Cloud Audit Logs
Fine-tuning ❌ ✅
Batch prediction ❌ ✅
Pricing Per token Per token (different rates)
Quotas Shared Project-level quotas

Setup

bash
pip install google-cloud-aiplatform
bash
# Authenticate
gcloud auth application-default login

# Or use service account
export GOOGLE_APPLICATION_CREDENTIALS=/path/to/service-account.json
bash
# Set project and location
export GOOGLE_CLOUD_PROJECT=my-project-id
export GOOGLE_CLOUD_LOCATION=us-central1

Instructions

Basic Gemini Inference

python
import vertexai
from vertexai.generative_models import GenerativeModel

vertexai.init(project="my-project-id", location="us-central1")

model = GenerativeModel("gemini-2.0-flash-001")
response = model.generate_content("Explain containerization in simple terms.")
print(response.text)

Multi-Modal Inference

python
import vertexai
from vertexai.generative_models import GenerativeModel, Part
import base64

vertexai.init(project="my-project-id", location="us-central1")
model = GenerativeModel("gemini-2.0-flash-001")

# Analyze image from Cloud Storage
gcs_image = Part.from_uri(
    uri="gs://my-bucket/product-photo.jpg",
    mime_type="image/jpeg",
)
response = model.generate_content(["Describe this product:", gcs_image])
print(response.text)

# Analyze local image
with open("chart.png", "rb") as f:
    image_data = f.read()

local_image = Part.from_data(data=image_data, mime_type="image/png")
response = model.generate_content(["What trends does this chart show?", local_image])
print(response.text)

Streaming Responses

python
import vertexai
from vertexai.generative_models import GenerativeModel

vertexai.init(project="my-project-id", location="us-central1")
model = GenerativeModel("gemini-2.0-flash-001")

for chunk in model.generate_content("Write a product description for a smartwatch.", stream=True):
    print(chunk.text, end="", flush=True)
print()

Chat Session

python
import vertexai
from vertexai.generative_models import GenerativeModel, ChatSession

vertexai.init(project="my-project-id", location="us-central1")

model = GenerativeModel(
    model_name="gemini-2.0-flash-001",
    system_instruction="You are a GCP expert. Provide concise, actionable answers.",
)

chat = model.start_chat()
print(chat.send_message("How do I set up Cloud Run?").text)
print(chat.send_message("What about environment variables?").text)

Function Calling

python
import vertexai
from vertexai.generative_models import (
    FunctionDeclaration,
    GenerativeModel,
    Tool,
)

vertexai.init(project="my-project-id", location="us-central1")

get_bq_query = FunctionDeclaration(
    name="run_bigquery_query",
    description="Run a SQL query on BigQuery and return results",
    parameters={
        "type": "object",
        "properties": {
            "query": {"type": "string", "description": "SQL query to execute"},
            "dataset": {"type": "string", "description": "BigQuery dataset name"},
        },
        "required": ["query"],
    },
)

tool = Tool(function_declarations=[get_bq_query])
model = GenerativeModel("gemini-2.0-flash-001", tools=[tool])

response = model.generate_content("How many users signed up last week?")

if response.candidates[0].function_calls:
    fc = response.candidates[0].function_calls[0]
    print(f"Function: {fc.name}, Args: {dict(fc.args)}")

Fine-Tuning Gemini

python
import vertexai
from vertexai.tuning import sft

vertexai.init(project="my-project-id", location="us-central1")

# Prepare training data in JSONL format in GCS:
# {"messages": [{"role": "user", "content": "..."}, {"role": "model", "content": "..."}]}

tuning_job = sft.train(
    source_model="gemini-2.0-flash-001",
    train_dataset="gs://my-bucket/training-data.jsonl",
    validation_dataset="gs://my-bucket/validation-data.jsonl",
    tuned_model_display_name="my-fine-tuned-gemini",
    epochs=3,
    learning_rate_multiplier=1.0,
)

print(f"Tuning job: {tuning_job.resource_name}")
print(f"State: {tuning_job.state}")

# Wait for completion
tuning_job.wait()
print(f"Tuned model: {tuning_job.tuned_model_name}")

Batch Prediction

python
import vertexai
from vertexai.generative_models import GenerativeModel
from vertexai.preview.batch_prediction import BatchPredictionJob

vertexai.init(project="my-project-id", location="us-central1")

# Input JSONL format in GCS:
# {"request": {"contents": [{"role": "user", "parts": [{"text": "Translate: Hello"}]}]}}

job = BatchPredictionJob.submit(
    source_model="gemini-2.0-flash-001",
    input_dataset="gs://my-bucket/batch-inputs.jsonl",
    output_uri_prefix="gs://my-bucket/batch-outputs/",
)

print(f"Batch job: {job.resource_name}")
job.wait()
print(f"Output: {job.output_location}")

IAM Setup for Service Account

bash
# Create a service account for your app
gcloud iam service-accounts create gemini-app-sa \
    --display-name="Gemini App Service Account"

# Grant Vertex AI User role
gcloud projects add-iam-policy-binding my-project-id \
    --member="serviceAccount:[email protected]" \
    --role="roles/aiplatform.user"

# Download key (for non-GCP environments)
gcloud iam service-accounts keys create key.json \
    --iam-account=gemini-app-sa@my-project-id.iam.gserviceaccount.com

VPC Service Controls (Enterprise Isolation)

python
# When VPC SC is enabled, all API calls must originate from within the perimeter
# Configure the SDK to use private endpoints:

import vertexai

vertexai.init(
    project="my-project-id",
    location="us-central1",
    api_endpoint="us-central1-aiplatform.googleapis.com",  # Regional endpoint
)

Available Gemini Models on Vertex AI

Model ID Notes
gemini-2.0-flash-001 Latest Flash, fast + capable
gemini-1.5-pro-002 2M context, most capable
gemini-1.5-flash-002 1M context, balanced
text-embedding-005 Latest embeddings (768 dims)

Use gemini-2.0-flash-001 (version pinned) in production to avoid unexpected model changes.

Guidelines

  • Always pin model versions (e.g., gemini-2.0-flash-001 not gemini-2.0-flash) in production for stability.
  • Use Application Default Credentials (gcloud auth application-default login) during development.
  • In GKE or Cloud Run, use Workload Identity — no service account keys needed.
  • Fine-tuning requires a training JSONL with messages format and at least 100 examples.
  • Batch prediction is cost-effective for offline bulk inference (no streaming).
  • Enable Cloud Audit Logs on the aiplatform.googleapis.com service for compliance.
  • Vertex AI supports regional endpoints — choose a region to ensure data residency compliance.

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