jeremy-vertex-terraform Verified Gold

Verified Gold · 94/100 devops v2.0.0 by Jeremy Longshore

Terraform configurations for Vertex AI platform and Agent Engine

1 Skills
MIT License
Free Pricing

Installation

Open Claude Code and run this command:

/plugin install jeremy-vertex-terraform@claude-code-plugins-plus

Use --global to install for all projects, or --project for current project only.

What It Does

This plugin provides Terraform modules for deploying Vertex AI services including Model Garden foundation models, Gemini API endpoints, vector search for RAG applications, ML pipelines, and production model serving infrastructure.

Key Infrastructure Components:

  • googlevertexai_endpoint for model serving
  • googlevertexaideployedmodel for model versions
  • googlevertexai_index for vector search
  • googlevertexaiindexendpoint for similarity search
  • googlevertexaifeaturestore for feature management
  • Cloud Storage for model artifacts
  • BigQuery for ML model training

Features

Model Garden Deployment: Foundation models (Gemini, PaLM, Claude, Llama)

Gemini API Endpoints: Dedicated endpoints with rate limiting

Vector Search: ScaNN-based similarity search for RAG

ML Pipelines: Kubeflow Pipelines for training workflows

Model Serving: Production endpoints with auto-scaling

Batch Predictions: Large-scale inference jobs

Feature Store: Centralized feature management

Monitoring: Model performance tracking and drift detection

Skills (1)

vertex-infra-expert SKILL.md View full skill →

Execute use when provisioning Vertex AI infrastructure with Terraform.

ReadWriteEditGrepGlobBash(terraform:*)Bash(gcloud:*)

How It Works

Natural Language Activation


"Create Terraform for Gemini endpoint deployment"
"Deploy vector search for RAG application"
"Set up Vertex AI Pipeline for model training"
"Create Feature Store for ML features"
"Deploy custom model to Vertex AI endpoint"

Use Cases

Gemini API Deployment


"Create Terraform for Gemini 2.0 Flash endpoint"
"Deploy Gemini Pro with auto-scaling"

Vector Search for RAG


"Set up vector search infrastructure for RAG application"
"Deploy embeddings index with 768 dimensions"

Custom Model Serving


"Deploy custom scikit-learn model to Vertex AI"
"Create endpoint for TensorFlow model with GPU"

Batch Predictions


"Set up batch prediction job for large dataset"
"Deploy batch inference with T4 GPUs"

Feature Store


"Create Feature Store for user features"
"Deploy feature serving for real-time predictions"

Ready to use jeremy-vertex-terraform?