Notatnik do tego ćwiczenia

Lab 3B: Training and Registering Models

Machine Learning is primarily about training models that you can use to provide predictive services to applications; so now it's time to see how you can use Azure Machine Learning jobs to run training scripts, and how to register the resulting trained models.

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: Use the Azure Machine Learning SDK to Train and Register Models

In this task, you'll use code in a notebook to run training scripts as Azure Machine Learning jobs, using mlflow.sklearn.autolog() to automatically log parameters, metrics, and the trained model, and then register the resulting model as diabetes_model.

1. In Azure Machine Learning studio, view the Compute page for your workspace; and on the Compute Instances tab, ensure your compute instance (mymachine) is running. If not, start it.
2. When the compute instance is running, click the JupyterLab link to open JupyterLab in a new browser tab.
3. In the JupyterLab file browser, open the MachineLearningCourse/materialy/azure-ml/en folder you cloned in Lab 1B (~/cloudfiles/code/Users/<your-user-name>/MachineLearningCourse/materialy/azure-ml/en), then open the 03B - Training Models.ipynb notebook. Confirm that it uses 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.