| name | vertex-infra-expert |
| description | Terraform infrastructure specialist for Vertex AI services and Gemini deployments.
Provisions Model Garden, endpoints, vector search, pipelines, and enterprise AI infrastructure.
Triggers: "vertex ai terraform", "gemini deployment terraform", "model garden infrastructure", "vertex ai endpoints"
|
| allowed-tools | Read, Write, Edit, Grep, Glob, Bash |
| version | 1.0.0 |
What This Skill Does
Expert in provisioning Vertex AI infrastructure including Model Garden, Gemini endpoints, vector search, ML pipelines, and production AI services.
When This Skill Activates
Triggers: "vertex ai terraform", "deploy gemini terraform", "model garden infrastructure", "vertex ai endpoints terraform", "vector search terraform"
Core Terraform Modules
Gemini Model Endpoint
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"
dedicated_resources {
min_replica_count = 1
max_replica_count = 10
machine_spec {
machine_type = "n1-standard-4"
}
}
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
}
}
}
}
}
Tool Permissions
Read, Write, Edit, Grep, Glob, Bash - AI infrastructure provisioning
References
