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

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Assessment

Difficulty
3/5
Estimated time
1-2 days
Newbie friendliness
45/100
Issue type
Documentation
Clarity
Mostly clear
Activity status
Stale
Tech stack
azure, jupyter-notebook, python
Domain
documentation, machine-learning

Research direction

Start with aml-pipelines-use-databricks-as-compute-target.ipynb and review the five Databricks scenarios listed in the issue. Trace the cells needed for each scenario, then make each use case independently understandable; done means a customer can identify and follow the cells for one scenario without navigating unrelated examples.

Written by the indexing model from the issue text.

Description

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
Dominant language
Jupyter Notebook
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Forks
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