Overview

Duration is 1 min

Use this lab to explore the impact of different ways of creating machine learning datasets.

What you'll learn

In this lab, you will learn the importance of repeatability 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.

Setup

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 Google Skills 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. Terraform Script

This lab is using a terraform script to create the Cloud Vertex AI instance you will need for this exercise.

The notebook instance will contain the github repository you need to complete this assignment. It should take 2 - 3 minutes for the instance to be ready.

Please wait before launching the Jupyter notebook, otherwise the script may be interrupted and the repository may not be cloned.

Task 2. Enable APIs

1. On the Navigation menu (), click APIs & services.

2. Scroll down and confirm that your APIs are enabled.

3. If an API is missing, click ENABLE APIS AND SERVICES at the top, search for the API by name, and enable it for your project.

• BigQuery

Task 3. Launch AI Platform notebooks

To launch Vertex AI Notebook:

1. In the Navigation menu, click Vertex AI > Workbench.

2. Click Open JupyterLab. A JupyterLab window opens in a new tab.

Terraform script has already cloned the GitHub repository, training-data-analyst, that you'll use in this lab.

Task 4. Repeatable splitting

Duration is 30 min

1. In the notebook interface, navigate to training-data-analyst > courses > machine_learning > deepdive2 > launching_into_ml > labs and open repeatable_splitting.ipynb.

2. In the notebook interface, click on Edit > Clear All Outputs (click on Edit, then in the drop-down menu, select Clear All Outputs).

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

Tip: To run the current cell, click the cell and press SHIFT+ENTER. Other cell commands are listed in the notebook UI under Run.

End your lab

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

You will be given an opportunity to rate the lab experience. Select the applicable number of stars, type a comment, and then click Submit.

The number of stars indicates the following:

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• 2 stars = Dissatisfied
• 3 stars = Neutral
• 4 stars = Satisfied
• 5 stars = Very satisfied

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