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Exercise 3: Automating ML Workflows with Pipelines

Before You Start

Before you start this exercise, ensure that you have completed the previous execise.

Task 1: Create an Azure ML Pipeline

In a real production environment, data preparation, model training, and web service deployment are often automated as part of a DevOps process.

  1. Start your Notebook VM and connect to Jupyter.
  2. In the /mlads-aml/notebooks folder, open the 03 - Automating ML Workflows with Pipelines.ipynb notebook.
  3. Read the notes in the notebook, running each code cell in turn.

Note: If you intend to continue straight to the next exercise, leave your Notebook VM running. If you're taking a break, you might want to close the Jupyter tabs and Stop your Notebook VM to avoid incurring unnecessary costs.