Google Cloud Cookbook

Chapter 10. AI/ML

Machine learning (ML) and AI hold an increasingly important place in enterprise applications. Google Cloud offers a number of AI and ML services, from pre-trained APIs that can be added to existing applications with a few lines of code to the full-featured Vertex AI platform that can be used to train and operationalize ML models in many frameworks.

With model training and tuning becoming more automated, in particular with tools like AutoML, organizations are focusing more on advanced concepts, including continuous retraining and deployment with MLOps, as well as deploying explainable AI in the enterprise. In this chapter, we will present a number of recipes, using Vertex AI, from setting up your customized environment to training and deploying your first model, to more specific techniques aimed at integrating other services. These recipes assume a basic understanding of typical Python data science tools—for example, Jupyter notebooks and the Pandas library.

All code samples for this chapter are in this book’s GitHub repository. You can follow along and copy the code for each recipe by going to the folder with that recipe’s number.

10.1 Creating a Vertex AI Notebook

Problem

You need a hosted Jupyter Notebook environment running in Google Cloud that can easily connect to other Google services to perform data and ML tasks.

Solution

You can create, customize, and connect to a Vertex AI notebook.

1. From the Google menu bar, select Vertex AI > Notebooks.

2. Choose ...