# yaml-language-server: $schema=../../schemas/@aws-mdaa/cli.json
# All resources will be deployed to the default region specified in the environment or AWS configurations.
region: default

# Tags applied to all deployed resources
tag_config_data:
  cost-center: '123456'
  project: mlops-platform
  environment: dev

## Pre-Deployment Instructions
#
# 1. Set 'organization' to a unique name (used as prefix for all resource names)
# 2. Set context values for your environment (VPC, subnets, project name)
# 3. Deploy: npx @aws-mdaa/cli deploy
#
# The MLOps module deploys a unified training + deployment pipeline.
# Monitoring is deployed by the deploy seed code alongside the endpoint.

# Failure to do so may result in global naming conflicts.
# TODO: Set a globally unique organization name (used in S3 bucket names and resource prefixes)
organization: "<YOUR_ORG_NAME>"

context:
  # SageMaker project identifiers
  # TODO: Set your SageMaker project name
  sagemaker_project_name: "<YOUR_PROJECT_NAME>"

  # VPC and networking
  # MDAA best practice: run SageMaker jobs inside your VPC for network isolation.
  # Ensure the VPC has the following endpoints provisioned:
  #   Gateway:   com.amazonaws.<region>.s3
  #   Interface: com.amazonaws.<region>.sagemaker.api
  #              com.amazonaws.<region>.sagemaker.runtime
  #              com.amazonaws.<region>.sts
  #              com.amazonaws.<region>.logs
  # TODO: Set your VPC ID
  vpc_id: "<YOUR_VPC_ID>"
  subnet_ids:
    # TODO: Set private subnet ID 1
    - "<YOUR_SUBNET_ID_1>"
    # TODO: Set private subnet ID 2
    - "<YOUR_SUBNET_ID_2>"
  security_group_ids:
    # TODO: Set a security group ID
    - "<YOUR_SECURITY_GROUP_ID>"

domains:
  # MLOps domain: unified training → deployment pipeline
  mlops:
    environments:
      dev:
        account: default
        modules:
          # Unified MLOps module: training pipeline + deployment pipeline
          # Training creates model package group, S3 bucket, CodePipeline
          # Deploy creates CodeBuild + EventBridge triggered by model approval
          # Monitoring is deployed by the deploy seed code alongside the endpoint
          core:
            module_path: "@aws-mdaa/sagemaker-mlops"
            module_configs:
              - ./mlops/mlops.yaml