This chapter covers
- Deploying a deep learning model in a simple web application on our local system
- An introduction to key Google Cloud concepts
- An introduction to Vertex AI, the machine learning environment in Google Cloud
- Deploying a deep learning model with a Vertex AI endpoint
- Adapting the web application to use a Vertex AI endpoint
- Getting generative AI assistance with Gemini for Google Cloud
In chapter 9, we reviewed a set of best practices for training a deep learning model with tabular data and introduced the Kuala Lumpur real estate price prediction problem as a challenging tabular problem because of its mixed-type features. In this chapter, we will take the model we trained in chapter 9 and deploy it in a simple web application. First, we will deploy it locally—that is, having both the web server and the trained model on our local system. Next, we will introduce Google Cloud as an alternative way to deploy our model. In fact, we will take the trained model and deploy it with an endpoint in Vertex AI, the machine learning environment in Google Cloud. Finally, we will examine how to use Google’s generative AI assistant Gemini on Google Cloud. The code described in this chapter is available at https://mng.bz/6e1A.