- A Cloud Guru
Deploying an ML Model with Cloud Run
Deploying a trained machine learning (ML) model to the cloud increases availability and performance. In this hands-on lab, you'll learn how to take the code from a pre-trained ML model, containerize the application, store that container in a registry, and then deploy the stored container on Google Cloud Run.
Table of Contents
Enable the Cloud Build and Cloud Run APIs.
Retrieve the Working Files
- Activate the Cloud Shell.
- Clone the desired repository.
- Change directory to the working files.
Containerize the App and Store the Disk Image
Use the appropriate
command invoking Cloud Build to containerize the web app and then store the resulting disk image in Container Registry. -
Deploy the Disk Image to Cloud Run
In Cloud Shell, execute the proper command to deploy the stored disk image to Cloud Run.
What's a lab?
Hands-on Labs are real environments created by industry experts to help you learn. These environments help you gain knowledge and experience, practice without compromising your system, test without risk, destroy without fear, and let you learn from your mistakes. Hands-on Labs: practice your skills before delivering in the real world.