vertex-infra-expert

jeremylongshore's avatarfrom jeremylongshore

Use when provisioning Vertex AI infrastructure with Terraform. Trigger with phrases like "vertex ai terraform", "deploy gemini terraform", "model garden infrastructure", "vertex ai endpoints terraform", or "vector search terraform". Provisions Model Garden models, Gemini endpoints, vector search indices, ML pipelines, and production AI services with encryption and auto-scaling.

⭐878stars🔀101forks📁View on GitHub🕐Updated Jan 4, 2026
[Cloud Infrastructure]

When & Why to Use This Skill

The Vertex Infra Expert skill automates the provisioning of Google Cloud Vertex AI infrastructure using Terraform. It streamlines the deployment of production-ready Gemini endpoints, Model Garden resources, and Vector Search indices while ensuring enterprise-grade security, auto-scaling, and encryption standards are met through Infrastructure as Code (IaC).

Use Cases

  • 1. Production AI Deployment: Rapidly provision Gemini and custom model endpoints with built-in auto-scaling and KMS encryption for high-availability applications.
  • 2. Vector Search Architecture: Automate the creation of high-performance indices for embeddings to power RAG (Retrieval-Augmented Generation) and semantic search systems.
  • 3. MLOps Pipeline Automation: Set up end-to-end ML pipelines and GCS storage buckets for model artifacts, ensuring consistent environments across dev, staging, and production.
  • 4. Security & Compliance Hardening: Implement IAM least privilege access and operational validation steps to secure AI services and sensitive model data.
namevertex-infra-expert
description|
allowed-toolsRead, Write, Edit, Grep, Glob, Bash(terraform:*), Bash(gcloud:*)
version1.0.0
authorJeremy Longshore <jeremy@intentsolutions.io>
licenseMIT

Vertex Infra Expert

Overview

Provision Vertex AI infrastructure with Terraform (endpoints, deployed models, vector search indices, pipelines) with production guardrails: encryption, autoscaling, IAM least privilege, and operational validation steps. Use this skill to generate a minimal working Terraform baseline and iterate toward enterprise-ready deployments.

Prerequisites

Before using this skill, ensure:

  • Google Cloud project with Vertex AI API enabled
  • Terraform 1.0+ installed
  • gcloud CLI authenticated with appropriate permissions
  • Understanding of Vertex AI services and ML models
  • KMS keys created for encryption (if required)
  • GCS buckets for model artifacts and embeddings

Instructions

  1. Define AI Services: Identify required Vertex AI components (endpoints, vector search, pipelines)
  2. Configure Terraform: Set up backend and define project variables
  3. Provision Endpoints: Deploy Gemini or custom model endpoints with auto-scaling
  4. Set Up Vector Search: Create indices for embeddings with appropriate dimensions
  5. Configure Encryption: Apply KMS encryption to endpoints and data
  6. Implement Monitoring: Set up Cloud Monitoring for model performance
  7. Apply IAM Policies: Grant least privilege access to AI services
  8. Validate Deployment: Test endpoints and verify model availability

Output

Error Handling

See {baseDir}/references/errors.md for comprehensive error handling.

Examples

See {baseDir}/references/examples.md for detailed examples.

Resources