--- name: vertex-ai-gemini description: >- 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. license: Apache-2.0 compatibility: "Python 3.9+ with google-cloud-aiplatform SDK and GCP credentials" metadata: author: terminal-skills version: "1.0.0" category: data-ai tags: ["vertex-ai", "google-cloud", "gemini", "enterprise", "mlops"] 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" agents: [claude-code, openai-codex, gemini-cli, cursor] --- # 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:gemini-app-sa@my-project-id.iam.gserviceaccount.com" \ --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.