A new team will train scikit-learn and XGBoost models in Databricks notebooks and track every run in MLflow. Clusters are terminated each evening and recreated each morning. The team does not want to reinstall the machine learning stack on every cluster start, and they do not want to add a %pip install cell to the top of each notebook.
Which compute choice meets these requirements with the least setup?