
Before you begin
- Labs create a Google Cloud project and resources for a fixed time
- Labs have a time limit and no pause feature. If you end the lab, you'll have to restart from the beginning.
- On the top left of your screen, click Start lab to begin
Create a Cloud Storage Bucket
/ 50
Create a dataset
/ 50
AutoML helps developers with limited ML expertise train high quality image recognition models. Once you upload images to the AutoML UI, you can generate predictions against a pre-trained model via an easy to use REST API. In this lab, you upload images to Cloud Storage and use them to generate predictions from a pre-trained AutoML model.
In this lab, you perform the following tasks:
Read these instructions. Labs are timed and you cannot pause them. The timer, which starts when you click Start Lab, shows how long Google Cloud resources are made available to you.
This hands-on lab lets you do the lab activities in a real cloud environment, not in a simulation or demo environment. It does so by giving you new, temporary credentials you use to sign in and access Google Cloud for the duration of the lab.
To complete this lab, you need:
Click the Start Lab button. If you need to pay for the lab, a dialog opens for you to select your payment method. On the left is the Lab Details pane with the following:
Click Open Google Cloud console (or right-click and select Open Link in Incognito Window if you are running the Chrome browser).
The lab spins up resources, and then opens another tab that shows the Sign in page.
Tip: Arrange the tabs in separate windows, side-by-side.
If necessary, copy the Username below and paste it into the Sign in dialog.
You can also find the Username in the Lab Details pane.
Click Next.
Copy the Password below and paste it into the Welcome dialog.
You can also find the Password in the Lab Details pane.
Click Next.
Click through the subsequent pages:
After a few moments, the Google Cloud console opens in this tab.
Cloud Shell is a virtual machine that is loaded with development tools. It offers a persistent 5GB home directory and runs on the Google Cloud. Cloud Shell provides command-line access to your Google Cloud resources.
Click Activate Cloud Shell at the top of the Google Cloud console.
Click through the following windows:
When you are connected, you are already authenticated, and the project is set to your Project_ID,
gcloud
is the command-line tool for Google Cloud. It comes pre-installed on Cloud Shell and supports tab-completion.
Output:
Output:
gcloud
, in Google Cloud, refer to the gcloud CLI overview guide.
For the AutoML environment, confirm that Cloud AutoML API is enabled, open the Vertex AI Dashboard, then create a storage bucket to store training images.
Cloud AutoML API lets you train high-quality custom machine learning models with minimum effort and machine learning expertise.
To confirm Cloud AutoML API is enabled:
In the Google Cloud console, in the Navigation menu (), select APIs & Services > Library.
In the Search for APIs & services field, type Cloud AutoML, then click Cloud AutoML API in the search results.
Confirm that the Cloud AutoML API is in the Enable state.
The Vertex AI Dashboard provides a centralized interface to access services to train and then use an image classification model.
To open the Vertex AI Dashboard:
The storage bucket holds your training images.
Click Check my progress to verify the objective.
To train a model to classify cloud images, you need labeled training data so the model can develop an understanding of the image features associated with different types of clouds. In this example your model learns to classify three different types of clouds: cirrus, cumulus, and cumulonimbus.
To put the training images in your Cloud Storage bucket:
The training images are publicly available in a Cloud Storage bucket.
gsutil
command line utility for Cloud Storage to copy the training images into your bucket:If you click on the individual image files in each folder you can see the photos you'll use to train the model for each type of cloud.
Now that your training data is in Cloud Storage, you need a way for AutoML to access it. Typically, you'd create a CSV file where each row contains a URL to a training image and the associated label for that image.
For this lab, the CSV file has been created for you; you just need to update it with your bucket name.
Once that command completes, click the Refresh button at the top of the Storage browser. Confirm that you see the data.csv
file in your bucket.
In the Navigation menu, click Vertex AI > Datasets.
At the top of the console, click Create.
Set Dataset Name to clouds.
Select Single-label classification.
Click Create.
Choose Select import files from Cloud Storage and then click Browse >
Click Continue.
It will take 2 - 5 minutes for your images to import.
Click Check my progress to verify the objective.
After the import completes, the Browse tab opens and shows all the images in your dataset.
Try filtering by different labels in the left menu (i.e. click cumulus) to review the training images:
If any images are labeled incorrectly you can click on the image to switch the label:
Given that model training requires significant time, this lab provides a pre-trained model to generate predictions. In the following section, you use this model to generate predictions on the images you uploaded to Cloud Storage.
There are a few ways to generate predictions. In this lab, you upload images and see how your model does classifying these two images (the first is a cirrus cloud, the second is a cumulonimbus).
CLOUD1-JSON
:Example output:
Expected Output:
Test your understanding of AutoML by completing the short quiz on the topics covered in this lab. Use the knowledge you have gained in the lab to generate predictions.
Check if the model can predict the type of cloud in the image:
CLOUD1-JSON
as the input file:Check if the model can predict the type of cloud in the image:
CLOUD2-JSON
as the input file.Congratulations! In this lab, you learned how to use AutoML to classify images of clouds. You first uploaded training images to Cloud Storage and created a CSV for AutoML to find these images. Then you generated predictions on new cloud images using a pre-trained model.
...helps you make the most of Google Cloud technologies. Our classes include technical skills and best practices to help you get up to speed quickly and continue your learning journey. We offer fundamental to advanced level training, with on-demand, live, and virtual options to suit your busy schedule. Certifications help you validate and prove your skill and expertise in Google Cloud technologies.
Manual Last Updated December 3, 2024
Lab Last Tested December 3, 2024
Copyright 2025 Google LLC All rights reserved. Google and the Google logo are trademarks of Google LLC. All other company and product names may be trademarks of the respective companies with which they are associated.
This content is not currently available
We will notify you via email when it becomes available
Great!
We will contact you via email if it becomes available
One lab at a time
Confirm to end all existing labs and start this one