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

Comienza con aml-pipelines-use-databricks-as-compute-target.ipynb y revisa los cinco escenarios de Databricks enumerados en el issue. Sigue las celdas necesarias para cada escenario y, después, haz que cada caso de uso sea comprensible de forma independiente; se considera terminado cuando un cliente puede identificar y seguir las celdas de un escenario sin tener que navegar por ejemplos no relacionados.

Escrito por el modelo de indexación a partir del texto del issue.