In this tutorial, you'll learn how to build a binary classification model from tabular data using Vertex AI.
To follow step-by-step guidance for this task directly in the Google Cloud console, click Guide me:
The entire process takes a couple of hours to complete. Most of that time is not active time; you can close your browser window and return to the task later.
The goal of the trained model is to predict whether a bank client will buy a term deposit (a type of investment) using features like age, income, and profession. This type of model can help banks determine who to focus their marketing resources on.
This tutorial uses the Bank marketing open-source dataset, which is available through a Creative Commons CCO: Public Domain license. The column names have been updated for clarity.
This tutorial has the following steps:
|1. Set up your project and environment||Set up your project and environment.|
|2. Create a dataset and train an AutoML classification model||Create a tabular dataset and train a classification model.|
|3. Deploy a model and request a prediction||Create an endpoint and deploy your model to the endpoint. After your model is deployed to this new endpoint, test your model by requesting a prediction.|
|4. Clean up your project||Clean up the Google Cloud resources that you created during this tutorial to avoid incurring unexpected charges from some of the resources.|
Begin the tutorial by setting up your project and environment.