Overview

tf.transform allows users to define preprocessing pipelines and run these using large scale data processing frameworks, while also exporting the pipeline in a way that can be run as part of a TensorFlow graph.

What you learn

• Preproccess data and engineer new features using TfTransform.
• Create and deploy Apache Beam pipeline.
• Use processed data to train taxifare model locally then serve a prediction.

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

2. On the Notebook instances page, click New Notebook > TensorFlow Enterprise > TensorFlow Enterprise 2.3 (with LTS) > Without GPUs.

3. In the New notebook instance dialog, confirm the name of the deep learning VM, if you don’t want to region and zone leave all settings as they are and then click Create.
   The new VM will take 2-3 minutes to start.

4. Click Open JupyterLab.
   A JupyterLab window will open in a new tab.

5. You will see Build recommended pop up, click Build. If you see the build failed, ignore it.

Task 2. Clone course repo within your Vertex AI 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. Exploring tf.transform

1. In the notebook interface, navigate to training-data-analyst > courses > machine_learning > deepdive2 > feature_engineering > labs > 5_tftransform_taxifare.ipynb.

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

3. Carefully read through the notebook instructions and fill in lines marked with #TODO where you need to complete the code as needed

Tip: To run the current cell you can click the cell and hit shift + enter. Other cell commands are found in the notebook UI under Run.

• Hints may also be provided for the tasks to guide you along. Highlight the text to read the hints (they are in white text).
• If you need more help, you may take a look at the complete solution by navigating to training-data-analyst > courses > machine_learning > deepdive2 > feature_engineering > solutions and opening 5_tftransform_taxifare.ipynb.

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.

For feedback, suggestions, or corrections, please use the Support tab.

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