Overview

Duration is 1 min

Repeatability is important in machine learning. If you do the same thing now and 5 minutes from now and get different answers, then it makes experimentation difficult. In other words, you will find it difficult to gauge whether a change you made has resulted in an improvement or not.

What you will need

• You need to be logged into GCP Console with your Qwiklabs generated account.

What you will learn

In this lab, you will learn how to:

• Explore the impact of different ways of creating machine learning datasets.

Setup and requirements

For each lab, you get a new Google Cloud project and set of resources for a fixed time at no cost.

1. Sign in to Qwiklabs using an incognito window.

2. Note the lab's access time (for example, 1:15:00), and make sure you can finish within that time.
   There is no pause feature. You can restart if needed, but you have to start at the beginning.

3. When ready, click Start lab.

4. Note your lab credentials (Username and Password). You will use them to sign in to the Google Cloud Console.

5. Click Open Google Console.

6. Click Use another account and copy/paste credentials for this lab into the prompts.
   If you use other credentials, you'll receive errors or incur charges.

7. Accept the terms and skip the recovery resource page.

Task 1. Launch Vertex AI notebooks

1. Click on the Navigation Menu.

2. Click Vertex AI > Dashboard.

3. Click Enable All Recommended APIs. This action will only take a few seconds.

4. Click Workbench from the left pane.

5. Click User-Managed Notebooks on the View ribbon.

6. Click on the + Create New icon on the top of the page.

7. For Environment select Python 3 (with Intel® MKL).

8. Click on the Advanced Options link at the bottom of the side window.

9. Set the Region to and Zone to .

10. Click Machine Type from the left pane. Select E2-standard and e2-standard-4 from the list of options.

11. At the bottom of the page, click Create. Notebook creation should take 4 to 7 minutes to complete.

12. After a few minutes, the Vertex AI console will have your instance name followed by Open Jupyterlab. Click Open Jupyterlab.

Your notebook environment is now set up.

Task 2. Clone course repo within your AI Platform notebooks instance

To clone the training-data-analyst notebook in your JupyterLab instance:

1. In JupyterLab, to open a new terminal, click the Terminal icon.

2. At the command-line prompt, run the following command:

   git clone https://github.com/GoogleCloudPlatform/training-data-analyst

3. To confirm that you have cloned the repository, double-click on the training-data-analyst directory and ensure that you can see its contents.
   The files for all the Jupyter notebook-based labs throughout this course are available in this directory.

Task 3. Create repeatable dataset splits

Duration is 15 min

1. In the notebook interface, navigate to training-data-analyst > courses > machine_learning > deepdive > 02_generalization and open repeatable_splitting.ipynb.

2. In the notebook interface, click on Edit > Clear All Outputs.

3. Now read the narrative and execute each cell in turn.

End your lab

When you have completed your lab, click End Lab. Google Cloud Skills Boost removes the resources you’ve used and cleans the account for you.

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