Deploying an ML Model with Cloud Run

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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.

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Lab Info

Level

Intermediate

Last updated

Apr 06, 2025

Duration

30m

Challenge

Enable APIs

Enable the Cloud Build and Cloud Run APIs.
Challenge

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