Databricks AML sample is confusing: need separate sample of individual use case

The GIHUB sample designed for multiple use-cases and some of the customers are getting confused and unable to follow exactly what are the specific cells they need to run for a specific scenario. Please review and ensure the notebook has clear steps for each scenario.

https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-databricks-as-compute-target.ipynb

currently this notebook has all these scenarios which is difficult to follow:

The notebook will show:

1. Running an arbitrary Databricks notebook that the customer has in Databricks workspace
2. Running an arbitrary Python script that the customer has in DBFS
3. Running an arbitrary Python script that is available on local computer (will upload to DBFS, and then run in Databricks)
4. Running a JAR job that the customer has in DBFS.
5. How to get run context in a Databricks interactive cluster

Commencez par aml-pipelines-use-databricks-as-compute-target.ipynb et examinez les cinq scénarios Databricks répertoriés dans l’issue. Suivez les cellules nécessaires pour chaque scénario, puis rendez chaque cas d’utilisation compréhensible indépendamment des autres ; le travail est terminé lorsqu’un client peut identifier et suivre les cellules d’un scénario sans parcourir d’exemples sans rapport.

Rédigé par le modèle d'indexation à partir du texte de l'issue.