GSP709

Overview

In this lab, you use the What-If Tool to identify potential biases in a model that was trained on a mortgage loan applications dataset.

Objectives

In this lab, you will perform the following tasks:

• Build a binary classification model using XGBoost.

• Deploy the model to Cloud AI Platform.

• Use the What-If Tool on the deployed model to search for biases.

Setup and requirements

Before you click the Start Lab button

Read these instructions. Labs are timed and you cannot pause them. The timer, which starts when you click Start Lab, shows how long Google Cloud resources will be made available to you.

This hands-on lab lets you do the lab activities yourself in a real cloud environment, not in a simulation or demo environment. It does so by giving you new, temporary credentials that you use to sign in and access Google Cloud for the duration of the lab.

To complete this lab, you need:

• Access to a standard internet browser (Chrome browser recommended).

• Time to complete the lab---remember, once you start, you cannot pause a lab.

How to start your lab and sign in to the Google Cloud Console

1. Click the Start Lab button. If you need to pay for the lab, a pop-up opens for you to select your payment method. On the left is the Lab Details panel with the following:

   • The Open Google Console button
   • Time remaining
   • The temporary credentials that you must use for this lab
   • Other information, if needed, to step through this lab

2. Click Open Google Console. The lab spins up resources, and then opens another tab that shows the Sign in page.

   Tip: Arrange the tabs in separate windows, side-by-side.

   Note: If you see the Choose an account dialog, click Use Another Account.

3. If necessary, copy the Username from the Lab Details panel and paste it into the Sign in dialog. Click Next.

4. Copy the Password from the Lab Details panel and paste it into the Welcome dialog. Click Next.

   Important: You must use the credentials from the left panel. Do not use your Google Cloud Skills Boost credentials. Note: Using your own Google Cloud account for this lab may incur extra charges.

5. Click through the subsequent pages:

   • Accept the terms and conditions.
   • Do not add recovery options or two-factor authentication (because this is a temporary account).
   • Do not sign up for free trials.

After a few moments, the Cloud Console opens in this tab.

Note: You can view the menu with a list of Google Cloud Products and Services by clicking the Navigation menu at the top-left.

Create a storage bucket

Create a bucket using the Cloud Console:

1. In the Cloud Console, on the Navigation menu, click Cloud Storage.

2. Click CREATE BUCKET.

3. Choose a Regional bucket and set a unique name (use your project ID because it is unique). Then, click CREATE.

Click Check my progress to verify the objective. Create cloud storage bucket

Start a JupyterLab Notebook instance

1. In the Google Cloud Console, on the Navigation Menu, click Vertex AI > Workbench.

2. On the Notebook instances page, click NEW NOTEBOOK.

3. In the Customize instance menu, select TensorFlow Enterprise and choose the version of TensorFlow Enterprise 2.6 (with LTS) > Without GPUs.

4. In the New notebook instance dialog, for Region, select us-central1, for Zone, select a zone within the selected region, leave all other fields with their default options, and click CREATE.

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

5. Click Open JupyterLab. Your notebook is now set up.

Click Check my progress to verify the objective. Start a JupyterLab Notebook Instance

Clone the sample code

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

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

2. To clone the training-data-analyst repo, type in the following command, and press Enter.

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

Click Check my progress to verify the objective. Clone the sample code

Explore the What-If Tool

1. Navigate to training-data-analyst > quests > dei and open xgboost_caip_e2e.ipynb.
2. Continue the lab in the notebook, and run each cell by clicking the Run ( ) icon at the top of the screen. Alternatively, you can execute the code in a cell with SHIFT + ENTER. Read the narrative and make sure you understand what's happening in each cell.

Click Check my progress to verify the objective. Execute code in JupyterLab notebook

Congratulations!

In this lab you used the What-If Tool to identify potential biases in a model that was trained on a mortgage loan applications dataset.

Finish Your Quest

This self-paced lab is part of the Qwiklabs Explore Machine Learning Models with Explainable AI Quest. A Quest is a series of related labs that form a learning path. Completing a Quest earns you a badge to recognize your achievement. You can make your badge (or badges) public and link to them in your online resume or social media account. Enroll in a Quest and get immediate completion credit if you've taken this lab. See other available Qwiklabs Quests.

Take your next lab

Continue your quest with Explore Machine Learning Models with Explainable AI: Challenge Lab, or check out these suggestions:

• Compare Cloud AI Platform Models using the What-If Tool to identify Potential Bias

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Manual Last Updated December 21, 2021

Lab Last Tested December 21, 2021

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