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Lab 6B: Publishing a Pipeline

After you've created a pipeline, you can deploy it behind a batch endpoint through which the pipeline can be initiated. This enables you to run the pipeline on-demand or at scheduled times.

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; and Lab 6A in which you create the pipeline you will deploy in this lab.

Task 1: Deploy a Pipeline as a Batch Endpoint

In this task, you'll deploy the pipeline you created in the previous lab behind a batch endpoint.

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 06B - Publishing 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.