Determining the right algorithm and preprocessing transformations for model training can involve a lot of guesswork experimentation.
In this lab, you'll use automated machine learning to determine the optimal algorithm and preprocessing steps for a model by performing multiple training runs in parallel.
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.
In this task, you'll use automated machine learning to determine the optimal algorithm and preprocessing transformations for model training.
- 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.
- When the compute instance is running, click the Jupyter link to open the Jupyter home page in a new browser tab.
- In the Jupyter home page, in the Users/DP100 folder, open the 08B - Using Automated Machine Learning.ipynb notebook. 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 Jupyter tabs and Stop your compute instance to avoid incurring unnecessary costs.