vertex-infra-expert

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

Gemini Model Endpoint:

# {baseDir}/terraform/vertex-endpoints.tf
resource "google_vertex_ai_endpoint" "gemini_endpoint" {
  name         = "gemini-25-flash-endpoint"
  display_name = "Gemini 2.5 Flash Production"
  location     = var.region

  encryption_spec {
    kms_key_name = google_kms_crypto_key.vertex_key.id
  }
}

resource "google_vertex_ai_deployed_model" "gemini_deployment" {
  endpoint = google_vertex_ai_endpoint.gemini_endpoint.id
  model    = "publishers/google/models/gemini-2.5-flash"

  automatic_resources {
    min_replica_count = 1
    max_replica_count = 5
  }
}

Vector Search Index:

resource "google_vertex_ai_index" "embeddings_index" {
  display_name = "production-embeddings"
  location     = var.region

  metadata {
    contents_delta_uri = "gs://${google_storage_bucket.embeddings.name}/index"
    config {
      dimensions = 768
      approximate_neighbors_count = 150
      distance_measure_type = "DOT_PRODUCT_DISTANCE"

      algorithm_config {
        tree_ah_config {
          leaf_node_embedding_count = 1000
          leaf_nodes_to_search_percent = 10
        }
      }
    }
  }
}

Error Handling

API Not Enabled

• Error: "Vertex AI API has not been used in project"
• Solution: Enable with gcloud services enable aiplatform.googleapis.com

Model Not Found

• Error: "Model publishers/google/models/... not found"
• Solution: Verify model ID and region availability

Quota Exceeded

• Error: "Quota exceeded for resource"
• Solution: Request quota increase or reduce replica count

KMS Key Access Denied

• Error: "Permission denied on KMS key"
• Solution: Grant cloudkms.cryptoKeyEncrypterDecrypter role to Vertex AI service account

Vector Search Build Failed

• Error: "Index build failed"
• Solution: Check GCS bucket permissions and embedding format

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/