After your AutoML image classification model is done training, use the
Google Cloud console to create an endpoint and deploy your model to
the endpoint. After your model is deployed to this new endpoint, send an image
to the model for label prediction.

This tutorial has several pages:

1. [Set up your project and environment.](https://docs.cloud.google.com/gemini-enterprise-agent-platform/machine-learning/tutorials/image-classification-automl.md.txt)

2. [Create an image classification dataset, and import images.](https://docs.cloud.google.com/gemini-enterprise-agent-platform/machine-learning/tutorials/image-classification-automl/dataset.md.txt)

3. [Train an AutoML image classification model.](https://docs.cloud.google.com/gemini-enterprise-agent-platform/machine-learning/tutorials/image-classification-automl/training.md.txt)

4. [Evaluate and analyze model performance.](https://docs.cloud.google.com/gemini-enterprise-agent-platform/machine-learning/tutorials/image-classification-automl/error-analysis.md.txt)

5. Deploy a model to an endpoint, and send a prediction.

6. [Clean up your project.](https://docs.cloud.google.com/gemini-enterprise-agent-platform/machine-learning/tutorials/image-classification-automl/cleanup.md.txt)

Each page assumes that you have already performed the instructions from the
previous pages of the tutorial.

## Deploy your model to an endpoint

Access your trained model to deploy it to a new or existing endpoint from
the Models page:

1. In the Google Cloud console, in the Gemini Enterprise Agent Platform section, go to
   the **Training** page.

   [Go to the Training page](https://console.cloud.google.com/agent-platform/training)
2. Select your trained AutoML model. This takes you to the **Evaluate** tab
   where you can view model performance metrics.

3. Choose the **Deploy \& test** tab.

4. Click **Deploy to endpoint**.

5. Choose **Create new
   endpoint** , set the endpoint name to `hello_automl_image`, then click
   **Continue**.

6. In **Model settings** , accept the **Traffic split** of
   **100%** , enter **1** in **Number of compute nodes** , then click **Done**.

7. Click **Deploy** to deploy your model to your new endpoint.

It takes several minutes to create the endpoint and deploy the AutoML model
to the new endpoint.

## Send a prediction to your model

After the endpoint creation process finishes you can send a single image
annotation (prediction) request in the Google Cloud console.

1. Navigate to the "Test your model" section of the same **Deploy \& test** tab
   you used to create an endpoint in the previous step
   (**Models \> <var translate="no">your_model</var> \> Deploy \& test**).

2. Click **Upload image** and choose a locally saved image for prediction, and
   view its predicted label.

   ![Image prediction in UI](https://docs.cloud.google.com/static/gemini-enterprise-agent-platform/machine-learning/tutorials/image-classification-automl/images/model-predict.png) *Image credit*: [Siming Ye, Unsplash](https://unsplash.com/photos/qE-_sYxOMa8) (*shown in UI view*).

## What's next

Follow the [last page of the tutorial](https://docs.cloud.google.com/gemini-enterprise-agent-platform/machine-learning/tutorials/image-classification-automl/cleanup.md.txt) to clean up
resources that you have created.