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Lab 6A: Creating a Pipeline

You can use the Azure Machine Learning SDK v2 to perform all of the tasks required to create and operate a machine learning solution in Azure. Rather than perform these tasks individually, you can use pipelines to orchestrate the components required to prepare data, run training scripts, register models, and other tasks.

Before You Start

Before you start this lab, ensure that you have completed Lab 1A and Lab 1B, which include tasks to create the Azure Machine Learning workspace and other resources used in this lab.

Task 1: Create a Pipeline

In this task, you'll create a pipeline to train and register a model.

1. In Azure Machine Learning studio, view the Compute page for your workspace; and on the Compute instances tab, ensure your compute instance is running. If not, start it.
2. When the compute instance is running, select the JupyterLab link for the compute instance to open JupyterLab in a new browser tab.
3. In the JupyterLab file browser, open the MachineLearningCourse/materialy/azure-ml/en folder (the repository you cloned in Lab 1B) and open the 06A - Creating a Pipeline.ipynb notebook. Confirm that it's using the Python 3.10 - SDK v2 kernel, then 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 compute instance running. If you're taking a break, you might want to close all JupyterLab tabs and Stop your compute instance to avoid incurring unnecessary costs.