# Test your deployment
<a name="canvas-deploy-model-test"></a>

You can test a model deployment by invoking the endpoint, or making single prediction requests, through the Amazon SageMaker Canvas application. You can use this functionality to confirm that your endpoint responds to requests before invoking your endpoint programmatically in a production environment.

## Test a custom model deployment
<a name="canvas-deploy-model-test-custom"></a>

You can test a custom model deployment by accessing it through the **ML Ops** page and making a single invocation, which returns a prediction along with the probability that the prediction is correct.

**Note**  
Execution length is an estimate of the time taken to invoke and get a response from the endpoint in Canvas. For detailed latency metrics, see [SageMaker AI Endpoint Invocation Metrics](https://docs.amazonaws.cn/sagemaker/latest/dg/monitoring-cloudwatch.html#cloudwatch-metrics-endpoint-invocation).

To test your endpoint through the Canvas application, do the following:

1. Open the SageMaker Canvas application.

1. In the left navigation panel, choose **ML Ops**.

1. Choose the **Deployments** tab.

1. From the list of deployments, choose the one with the endpoint that you want to invoke.

1. On the deployment’s details page, choose the **Test deployment** tab.

1. On the deployment testing page, you can modify the **Value** fields to specify a new data point. For time series forecasting models, you specify the **Item ID** for which you want to make a forecast.

1. After modifying the values, choose **Update** to get the prediction result.

The prediction loads, along with the **Invocation result** fields which indicate whether or not the invocation was successful and how long the request took to process.

The following screenshot shows a prediction performed in the Canvas application on the **Test deployment** tab.

![\[The Canvas application showing a test prediction for a deployed model.\]](http://docs.amazonaws.cn/en_us/sagemaker/latest/dg/images/studio/canvas/canvas-test-deployments.png)


For all model types except numeric prediction and time series forecasting, the prediction returns the following fields:
+  **predicted\$1label** – the predicted output
+  **probability** – the probability that the predicted label is correct
+  **labels** – the list of all the possible labels
+  **probabilities** – the probabilities corresponding to each label (the order of this list matches the order of the labels)

For numeric prediction models, the prediction only contains the **score** field, which is the predicted output of the model, such as the predicted price of a house.

For time series forecasting models, the prediction is a graph showing the forecasts by quantile. You can choose **Schema view** to see the forecasted numeric values for each quantile.

You can continue making single predictions through the deployment testing page, or you can see the following section [Invoke your endpoint](canvas-deploy-model-invoke.md) to learn how to invoke your endpoint programmatically from applications.

## Test a JumpStart foundation model deployment
<a name="canvas-deploy-model-test-js"></a>

You can chat with a deployed JumpStart foundation model through the Canvas application to test its functionality before invoking it through code.

To chat with a deployed JumpStart foundation model, do the following:

1. Open the SageMaker Canvas application.

1. In the left navigation panel, choose **ML Ops**.

1. Choose the **Deployments** tab.

1. From the list of deployments, find the one that you want to invoke and choose its **More options** icon (![\[More options icon for a model deployment.\]](http://docs.amazonaws.cn/en_us/sagemaker/latest/dg/images/studio/canvas/more-options-icon.png)).

1. From the context menu, choose **Test deployment**.

1. A new **Generate, extract and summarize content** chat opens with the JumpStart foundation model, and you can begin typing prompts. Note that prompts from this chat are sent as requests to your SageMaker AI Hosting endpoint.