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
- Define AI Services: Identify required Vertex AI components (endpoints, vector search, pipelines)
- Configure Terraform: Set up backend and define project variables
- Provision Endpoints: Deploy Gemini or custom model endpoints with auto-scaling
- Set Up Vector Search: Create indices for embeddings with appropriate dimensions
- Configure Encryption: Apply KMS encryption to endpoints and data
- Implement Monitoring: Set up Cloud Monitoring for model performance
- Apply IAM Policies: Grant least privilege access to AI services
- Validate Deployment: Test endpoints and verify model availability
Output
- Configuration files or code changes applied to the project
- Validation report confirming correct implementation
- Summary of changes made and their rationale
See Terraform implementation details for output format specifications.
Error Handling
See ${CLAUDE_SKILL_DIR}/references/errors.md for comprehensive error handling.
Examples
See ${CLAUDE_SKILL_DIR}/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:
- Terraform examples in ${CLAUDE_SKILL_DIR}/vertex-examples/