What tools does this skill require?

It requires Terraform 1.0+, the gcloud CLI, and an active Google Cloud project with the Vertex AI (aiplatform.googleapis.com) API enabled.

Does it handle security for AI resources?

Absolutely. It includes configurations for Customer-Managed Encryption Keys (CMEK) via KMS and enforces IAM least-privilege policies for service accounts.

Can this skill help with Gemini model deployments?

Yes, it provides HCL templates for deploying Gemini and other Model Garden models to Vertex AI endpoints with configurable auto-scaling.

Can I use it for Vector Search setup?

Yes, it supports the creation of Vertex AI Vector Search indices, including configuration for dimensions, distance measures, and GCS metadata sources.

Vertex AI Infrastructure Expert

Name: Vertex AI Infrastructure Expert
Author: BbgnsurfTech

byBbgnsurfTech

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Deployment & DevOps

Automates the provisioning of production-ready Vertex AI infrastructure on Google Cloud using Terraform and HCL best practices.

About

Vertex AI Infrastructure Expert is a specialized skill designed to streamline the deployment of Google Cloud's machine learning services through Infrastructure as Code (IaC). It enables developers to rapidly configure and provision complex AI components, including Gemini model endpoints, Model Garden resources, and Vector Search indices, while ensuring security through KMS encryption and IAM least-privilege policies. Whether you are setting up scalable ML pipelines or production-grade embedding search, this skill provides the necessary Terraform templates and gcloud command integration to maintain consistent, enterprise-ready AI environments.

Key Features

1 GitHub stars
Implement enterprise-grade security with KMS encryption and IAM role assignment
Configure high-performance Vertex AI Vector Search indices for RAG applications
Standardize AI infrastructure across environments with reusable HCL modules
Provision Gemini and Model Garden endpoints with automatic scaling resources
Automate the setup of Vertex AI Pipelines and integrated Cloud Monitoring

Use Cases

Setting up a vector database infrastructure for large-scale semantic search
Deploying a production-scale Gemini 1.5 Flash model endpoint with auto-scaling
Automating the provisioning of MLOps pipelines on Google Cloud Platform

About

Key Features

1 GitHub stars
Implement enterprise-grade security with KMS encryption and IAM role assignment
Configure high-performance Vertex AI Vector Search indices for RAG applications
Standardize AI infrastructure across environments with reusable HCL modules
Provision Gemini and Model Garden endpoints with automatic scaling resources
Automate the setup of Vertex AI Pipelines and integrated Cloud Monitoring

Use Cases

Setting up a vector database infrastructure for large-scale semantic search
Deploying a production-scale Gemini 1.5 Flash model endpoint with auto-scaling
Automating the provisioning of MLOps pipelines on Google Cloud Platform