This lab will give you hands-on practice with TensorFlow 2.x model training, both locally and on AI Platform. After training, you will learn how to deploy your model to AI Platform for serving (prediction). You'll train your model to predict income category of a person using the United States Census Income Dataset.

This lab gives you an introductory, end-to-end experience of training and prediction on AI Platform. The lab will use a census dataset to:

• Create a TensorFlow 2.x training application and validate it locally.
• Run your training job on a single worker instance in the cloud.
• Deploy a model to support prediction.
• Request an online prediction and see the response.

What you will build

The sample builds a classification model for predicting income category based on United States Census Income Dataset. The two income categories (also known as labels) are:

• >50K — Greater than 50,000 dollars
• <=50K — Less than or equal to 50,000 dollars

The sample defines the model using the Keras Sequential API. The sample defines the data transformations particular to the census dataset, then assigns these (potentially) transformed features to either the DNN or the linear portion of the model.

Launch AI Platform Notebooks

To launch AI Platform Notebooks:

1. Click on the Navigation Menu and navigate to AI Platform, then to Notebooks.

https://cdn.qwiklabs.com/fnUEPKKDGG4Xw1nbWJRpVfg02LTmJLOrel2Ny42JQVk%3D

2. On the Notebook instances page, click New Instance. Select the latest version of TensorFlow Enterprise 2.x Without GPUs.

https://cdn.qwiklabs.com/XoalHYoeOlvhs7jtFOoGRhIlXS%2FrGGbFaVnsgDMNHZ4%3D

In the pop-up, confirm the name of the deep learning VM, for Region, select us-central1 and for Zone, select a zone within that region. Leave the remaining fields with their default and click Create.

The new VM will take 2-3 minutes to start.

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