Overview

In this lab you train, evaluate, and deploy a machine learning model to predict a baby's weight.

What you learn

In this lab, you:

• Launch Vertex AI Workbench instance
• Create a BigQuery Dataset and GCS Bucket
• Export from BigQuery to CSVs in GCS
• Training on Cloud AI Platform
• Deploy trained model

Setup your lab

Start your lab

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.

Enable the AI Platform Training & Prediction API

1. In the API library, Search for "AI Platform Training & Prediction API" and click the AI Platform Training & Prediction API card.

2. Click Enable to activate the API. If you see Manage, the API is already activated.

Task 1. Create storage bucket

1. In the Google Cloud Console, on the Navigation menu (), click Cloud Storage > Buckets.

2. Click + Create.

3. Type a unique name for your bucket, such as your project ID.

4. Click Create.

5. Confirm Enforce public access prevention on this bucket on "Public access will be prevented" pop-up.

Task 2. Launch Vertex AI Workbench instance

1. In the Google Cloud console, from the Navigation menu (), select Vertex AI > Dashboard.

2. Click Enable All Recommended APIs.

3. In the Navigation menu, click Workbench.

   At the top of the Workbench page, ensure you are in the Instances view.

4. Click Create New.

5. Configure the Instance:

   • Name: lab-workbench
   • Region: Set the region to
   • Zone: Set the zone to
   • Advanced Options (Optional): If needed, click "Advanced Options" for further customization (e.g., machine type, disk size).

6. Click Create.

This will take a few minutes to create the instance. A green checkmark will appear next to its name when it's ready.

7. Click Open Jupyterlab next to the instance name to launch the JupyterLab interface. This will open a new tab in your browser.

8. Click the Python 3 icon to launch a new Python notebook.

9. Right-click on the Untitled.ipynb file in the menu bar and select Rename Notebook to give it a meaningful name.

Your environment is set up. You are now ready to start working with your Vertex AI Workbench notebook.

Click Check my progress to verify the objective. Launch Vertex AI Workbench instance

Task 3. Clone course repo within your Vertex AI Workbench instance

The GitHub repo contains both the lab file and solutions files for the course.

1. Copy and run the following code in the first cell of your notebook to clone the training-data-analyst repository.

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

2. Confirm that you have cloned the repository. Double-click on the training-data-analyst directory and ensure that you can see its contents.

Click Check my progress to verify the objective. Clone course repo within your Vertex AI Workbench instance

Task 4. Structured data prediction using Vertex AI Platform

1. In the notebook interface, navigate to training-data-analyst > courses > machine_learning > deepdive2 > production_ml > babyweight, and open train_deploy.ipynb.

2. In the Select Kernel dialog, choose Python 3 from the list of available kernels.

3. From the menu, click Edit > Clear All Outputs.

4. Read the narrative and click Shift + Enter (or Run) on each cell in the notebook.

Task 5. Test your knowledge

Test your knowledge about Google cloud Platform by taking our quiz.

Congratulations!

You learned how to train, evaluate, and deploy a machine learning model in Vertex AI notebooks.

End your lab

When you have completed your lab, click End Lab. Qwiklabs 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:

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

You can close the dialog box if you don't want to provide feedback.

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