cookiecutter.json

{
  "project_name": "my-mlops-project",
  "cloud": ["azure", "aws"],
  "cicd_platform": ["GitHub Actions", "Azure DevOps"],
  "mlflow_experiment_parent_dir":  "Parent Databricks workspace directory under which MLflow experiment for the current project will be created. Default: `/{{cookiecutter.project_name}}`",
  "databricks_staging_workspace_host": "URL of staging Databricks workspace, used to run CI tests on PRs and preview config changes before they're deployed to production. Default: `{%- if cookiecutter.cloud == 'azure' -%} https://adb-xxxx.xx.azuredatabricks.net {%- elif cookiecutter.cloud == 'aws' -%} https://your-staging-workspace.cloud.databricks.com {%- endif -%}`",
  "databricks_prod_workspace_host": "URL of production Databricks workspace. Default: `{%- if cookiecutter.cloud == 'azure' -%} https://adb-xxxx.xx.azuredatabricks.net {%- elif cookiecutter.cloud == 'aws' -%} https://your-prod-workspace.cloud.databricks.com {%- endif -%}`",
  "default_branch": "Name of the default branch, where the prod and staging ML resources are deployed from and the latest ML code is staged. Default: `main`",
  "release_branch": "Name of the release branch. The production jobs (model training, batch inference) defined in this stack pull ML code from this branch. Default: `release`",
  "read_user_group": "Display name of the user group to give read permissions to the ML jobs, integration test job runs, and machine learning resources created for this project. A group with this name must exist in both the staging and prod workspaces. Default: `users`",
  "_extensions": ["local_extensions.generate_doc_link", "local_extensions.regex_replace", "local_extensions.get_host"]
}