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.
npx skills add majiayu000/claude-skill-registry --skill vertex-ai-gemini --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 impor
- Overview
- Vertex AI vs Google AI Studio
- Setup
- Instructions
- Basic Gemini Inference
- Multi-Modal Inference
- Streaming Responses
- Chat Session
- Function Calling
- Fine-Tuning Gemini
- Batch Prediction
- IAM Setup for Service Account
- VPC Service Controls (Enterprise Isolation)
- Available Gemini Models on Vertex AI
pip install google-cloud-aiplatform Authenticate gcloud auth application-default login Or use service account export GOOGLE_APPLICATION_CREDENTIALS=/path/to/service-account.json Set project and location export GOOGLE_CLOUD_PROJECT=my-project-id export GOOGLE_CLOUD_LOCATION=us-central1 Create a service account for your app gcloud iam service-accounts create gemini-app-sa \
What does the vertex-ai-gemini skill do?
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.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill vertex-ai-gemini --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.