Notatnik do tego ćwiczenia

Lab 4B: Working with Datasets

Data assets provide a way to encapsulate data for jobs and training. You can use tabular (mltable) and file (uri_folder) data assets to define versioned sources of data that can easily be consumed in jobs.

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: Work with Data Assets

In this task, you'll use code in a notebook to work with tabular and file data assets.

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, then open the 04B - Working with Datasets.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.