databricks.yml

# The name of the bundle. run `databricks bundle schema` to see the full bundle settings schema.
bundle:
  name: my_mlops_stacks

variables:
  experiment_name:
    description: Experiment name for the model training.
    default: /Users/${workspace.current_user.userName}/${bundle.target}-my_mlops_stacks-experiment
  model_name:
    description: Model name for the model training.
    default: ${bundle.target}-my_mlops_stacks-model
  cluster_id:
    description: Cluster id for compute.

include:
  # Resources folder contains ML artifact resources for the ML project that defines model and experiment
  # And workflows resources for the ML project including model training -> validation -> deployment,
  # feature engineering,  batch inference, data monitoring, metric refresh, alerts and triggering retraining
  - ./resources/*.yml

# Deployment Target specific values for workspace
targets:
  dev: 
    default: true
    variables:
      cluster_id: 
    workspace:
      # TODO: add dev workspace URL
      host:



  staging:
    variables:
        cluster_id: 
    workspace:
      host: https://adb-xxxx.xx.azuredatabricks.net

  prod:
    variables:
      cluster_id:
    workspace:
      host: https://adb-xxxx.xx.azuredatabricks.net

  test:
    variables:
      cluster_id:
    workspace:
      host: https://adb-xxxx.xx.azuredatabricks.net