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

• Vertex AI Terraform: https://registry.terraform.io/providers/hashicorp/google/latest/docs/resources/vertex_ai_endpoint
• Vertex AI documentation: https://cloud.google.com/vertex-ai/docs
• Model Garden: https://cloud.google.com/model-garden
• Vector Search guide: https://cloud.google.com/vertex-ai/docs/vector-search
• Terraform examples in {baseDir}/vertex-examples/