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Labs

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.

Labs

Path Info

Level
Clock icon Intermediate
Duration
Clock icon 30m
Last updated
Clock icon Apr 06, 2025

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Table of Contents

  1. Challenge

    Enable APIs

    Enable the Cloud Build and Cloud Run APIs.

  2. Challenge

    Retrieve the Working Files

    1. Activate the Cloud Shell.
    2. Clone the desired repository.
    3. Change directory to the working files.
  3. Challenge

    Containerize the App and Store the Disk Image

    Use the appropriate gcloud command invoking Cloud Build to containerize the web app and then store the resulting disk image in Container Registry.

  4. Challenge

    Deploy the Disk Image to Cloud Run

    In Cloud Shell, execute the proper command to deploy the stored disk image to Cloud Run.

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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.

Provided environment for hands-on practice

We will provide the credentials and environment necessary for you to practice right within your browser.

Guided walkthrough

Follow along with the author’s guided walkthrough and build something new in your provided environment!

Did you know?

On average, you retain 75% more of your learning if you get time for practice.