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

Inizia con aml-pipelines-use-databricks-as-compute-target.ipynb e analizza i cinque scenari Databricks elencati nell’issue. Traccia le celle necessarie per ogni scenario, quindi rendi ogni caso d’uso comprensibile in modo indipendente; il lavoro è completato quando un cliente può identificare e seguire le celle di uno scenario senza dover navigare tra esempi non correlati.

Scritto dal modello di indicizzazione a partire dal testo della issue.