# MLOps
After building a model in SageMaker Canvas that you feel confident about, you might want to integrate your model with the machine learning operations (MLOps) processes in your organization. MLOps includes common tasks such as deploying a model for use in production or setting up continuous integration and continuous deployment (CI/CD) pipelines.
The following topics describe how you can use features within Canvas to use a Canvas-built model in production.
**Topics**
+ [
# Register a model version in the SageMaker AI model registry
](canvas-register-model.md)
+ [
# Deploy your models to an endpoint
](canvas-deploy-model.md)
+ [
# View your deployments
](canvas-deploy-model-view.md)
+ [
# Update a deployment configuration
](canvas-deploy-model-update.md)
+ [
# Test your deployment
](canvas-deploy-model-test.md)
+ [
# Invoke your endpoint
](canvas-deploy-model-invoke.md)
+ [
# Delete a model deployment
](canvas-deploy-model-delete.md)
# Register a model version in the SageMaker AI model registry
With SageMaker Canvas, you can build multiple iterations, or versions, of your model to improve it over time. You might want to build a new version of your model if you acquire better training data or if you want to attempt to improve the model’s accuracy. For more information about adding versions to your model, see [Update a model](https://docs.amazonaws.cn/sagemaker/latest/dg/canvas-update-model.html).
After you’ve [built a model](https://docs.amazonaws.cn/sagemaker/latest/dg/canvas-build-model.html) that you feel confident about, you might want to evaluate its performance and have it reviewed by a data scientist or MLOps engineer in your organization before using it in production. To do this, you can register your model versions to the [SageMaker Model Registry](https://docs.amazonaws.cn/sagemaker/latest/dg/model-registry.html). The SageMaker Model Registry is a repository that data scientists or engineers can use to catalog machine learning (ML) models and manage model versions and their associated metadata, such as training metrics. They can also manage and log the approval status of a model.
After you register your model versions to the SageMaker Model Registry, a data scientist or your MLOps team can access the SageMaker Model Registry through [SageMaker Studio Classic](https://docs.amazonaws.cn/sagemaker/latest/dg/studio.html), which is a web-based integrated development environment (IDE) for working with machine learning models. In the SageMaker Model Registry interface in Studio Classic, the data scientist or MLOps team can evaluate your model and update its approval status. If the model doesn’t perform to their requirements, the data scientist or MLOps team can update the status to `Rejected`. If the model does perform to their requirements, then the data scientist or MLOps team can update the status to `Approved`. Then, they can [deploy your model to an endpoint](https://docs.amazonaws.cn/sagemaker/latest/dg/deploy-model.html#deploy-model-prereqs) or [automate model deployment](https://www.amazonaws.cn/blogs/machine-learning/building-automating-managing-and-scaling-ml-workflows-using-amazon-sagemaker-pipelines/) with CI/CD pipelines. You can use the SageMaker AI model registry feature to seamlessly integrate models built in Canvas with the MLOps processes in your organization.
The following diagram summarizes an example of registering a model version built in Canvas to the SageMaker Model Registry for integration into an MLOps workflow.
![\[The steps registering a model version built in Canvas for integration into an MLOps workflow.\]](http://docs.amazonaws.cn/en_us/sagemaker/latest/dg/images/studio/canvas/canvas-model-registration-diagram.jpg)
You can register tabular, image, and text model versions to the SageMaker Model Registry. This includes time series forecasting models and JumpStart based [fine-tuned foundation models](https://docs.amazonaws.cn/sagemaker/latest/dg/canvas-fm-chat-fine-tune.html).
**Note**
Currently, you can't register Amazon Bedrock based fine-tuned foundation models built in Canvas to the SageMaker Model Registry.
The following sections show you how to register a model version to the SageMaker Model Registry from Canvas.
## Permissions management
By default, you have permissions to register model versions to the SageMaker Model Registry. SageMaker AI grants these permissions for all new and existing Canvas user profiles through the [AmazonSageMakerCanvasFullAccess](https://docs.amazonaws.cn/aws-managed-policy/latest/reference/AmazonSageMakerCanvasFullAccess.html) policy, which is attached to the Amazon IAM execution role for the SageMaker AI domain that hosts your Canvas application.
If your Canvas administrator is setting up a new domain or user profile, when they're setting up the domain and following the prerequisite instructions in the [Getting started guide](https://docs.amazonaws.cn/sagemaker/latest/dg/canvas-getting-started.html#canvas-prerequisites), SageMaker AI turns on the model registration permissions through the **ML Ops permissions configuration** option, which is enabled by default.
The Canvas administrator can manage model registration permissions at the user profile level as well. For example, if the administrator wants to grant model registration permissions to some user profiles but remove permissions for others, they can edit the permissions for a specific user. The following procedure shows how to turn off model registration permissions for a specific user profile:
1. Open the SageMaker AI console at [https://console.amazonaws.cn/sagemaker/](https://console.amazonaws.cn/sagemaker/).
1. On the left navigation pane, choose **Admin configurations**.
1. Under **Admin configurations**, choose **domains**.
1. From the list of domains, select the user profile’s domain.
1. On the **domain details** page, choose the **User profile** whose permissions you want to edit.
1. On the **User Details** page, choose **Edit**.
1. In the left navigation pane, choose **Canvas settings**.
1. In the **ML Ops permissions configuration** section, turn off the **Enable Model Registry registration permissions** toggle.
1. Choose **Submit** to save the changes to your domain settings.
The user profile should no longer have model registration permissions.
## Register a model version to the SageMaker AI model registry
SageMaker Model Registry tracks all of the model versions that you build to solve a particular problem in a *model group*. When you build a SageMaker Canvas model and register it to SageMaker Model Registry, it gets added to a model group as a new model version. For example, if you build and register four versions of your model, then a data scientist or MLOps team working in the SageMaker Model Registry interface can view the model group and review all four versions of the model in one place.
When registering a Canvas model to the SageMaker Model Registry, a model group is automatically created and named after your Canvas model. Optionally, you can rename it to a name of your choice, or use an existing model group in the SageMaker Model Registry. For more information about creating a model group, see [Create a Model Group](https://docs.amazonaws.cn/sagemaker/latest/dg/model-registry-model-group.html).
**Note**
Currently, you can only register models built in Canvas to the SageMaker Model Registry in the same account.
To register a model version to the SageMaker Model Registry from the Canvas application, use the following procedure:
1. Open the SageMaker Canvas application.
1. In the left navigation pane, choose **My models**.
1. On the **My models** page, choose your model. You can **Filter by problem type** to find your model more easily.
1. After choosing your model, the **Versions** page opens, listing all of the versions of your model. You can turn on the **Show advanced metrics** toggle to view the advanced metrics, such as **Recall** and **Precision**, to compare your model versions and determine which one you’d like to register.
1. From the list of model versions, for the the version that you want to register, choose the **More options** icon (![\[Vertical ellipsis icon representing a menu or more options.\]](http://docs.amazonaws.cn/en_us/sagemaker/latest/dg/images/studio/canvas/more-options-icon.png)). Alternatively, you can double click on the version that you need to register, and then on the version details page, choose the **More options** icon (![\[Vertical ellipsis icon representing a menu or more options.\]](http://docs.amazonaws.cn/en_us/sagemaker/latest/dg/images/studio/canvas/more-options-icon.png)).
1. In the dropdown list, choose **Add to Model Registry**. The **Add to Model Registry** dialog box opens.
1. In the **Add to Model Registry** dialog box, do the following:
1. (Optional) In the **SageMaker Studio Classic model group** section, for the **Model group name** field, enter the name of the model group to which you want to register your version. You can specify the name for a new model group that SageMaker AI creates for you, or you can specify an existing model group. If you don’t specify this field, Canvas registers your version to a default model group with the same name as your model.
1. Choose **Add**.
Your model version should now be registered to the model group in the SageMaker Model Registry. When you register a model version to a model group in the SageMaker Model Registry, all subsequent versions of the Canvas model are registered to the same model group (if you choose to register them). If you register your versions to a different model group, you need to go to the SageMaker Model Registry and [delete the model group](https://docs.amazonaws.cn/sagemaker/latest/dg/model-registry-delete-model-group.html). Then, you can re-register your model versions to the new model group.
To view the status of your models, you can return to the **Versions** page for your model in the Canvas application. This page shows you the **Model Registry** status of each version. If the status is `Registered`, then the model has been successfully registered.
If you want to view the details of your registered model version, for the **Model Registry** status, you can hover over the **Registered** field to see the **Model registry details** pop-up box. These details contain more info, such as the following:
+ The **Model package group name** is the model group that your version is registered to in the SageMaker Model Registry.
+ The **Approval status**, which can be `Pending Approval`, `Approved`, or `Rejected`. If a Studio Classic user approves or rejects your version in the SageMaker Model Registry, then this status is updated on your model versions page when you refresh the page.
The following screenshot shows the **Model registry details** box, along with an **Approval status** of `Approved` for this particular model version.
![\[Screenshot of the SageMaker Model Registry details box in the Canvas application.\]](http://docs.amazonaws.cn/en_us/sagemaker/latest/dg/images/studio/canvas/approved-mr.png)
# Deploy your models to an endpoint
In Amazon SageMaker Canvas, you can deploy your models to an endpoint to make predictions. SageMaker AI provides the ML infrastructure for you to host your model on an endpoint with the compute instances that you choose. Then, you can *invoke* the endpoint (send a prediction request) and get a real-time prediction from your model. With this functionality, you can use your model in production to respond to incoming requests, and you can integrate your model with existing applications and workflows.
To get started, you should have a model that you'd like to deploy. You can deploy custom model versions that you've built, Amazon SageMaker JumpStart foundation models, and fine-tuned JumpStart foundation models. For more information about building a model in Canvas, see [How custom models work](canvas-build-model.md). For more information about JumpStart foundation models in Canvas, see [Generative AI foundation models in SageMaker Canvas](canvas-fm-chat.md).
Review the following **Permissions management** section, and then begin creating new deployments in the **Deploy a model** section.
## Permissions management
By default, you have permissions to deploy models to SageMaker AI Hosting endpoints. SageMaker AI grants these permissions for all new and existing Canvas user profiles through the [AmazonSageMakerCanvasFullAccess](https://docs.amazonaws.cn/aws-managed-policy/latest/reference/AmazonSageMakerCanvasFullAccess.html) policy, which is attached to the Amazon IAM execution role for the SageMaker AI domain that hosts your Canvas application.
If your Canvas administrator is setting up a new domain or user profile, when they're setting up the domain and following the prerequisite instructions in the [Prerequisites for setting up Amazon SageMaker Canvas](canvas-getting-started.md#canvas-prerequisites), SageMaker AI turns on the model deployment permissions through the **Enable direct deployment of Canvas models** option, which is enabled by default.
The Canvas administrator can manage model deployment permissions at the user profile level as well. For example, if the administrator doesn't want to grant model deployment permissions to all user profiles when setting up a domain, they can grant permissions to specific users after creating the domain.
The following procedure shows how to modify the model deployment permissions for a specific user profile:
1. Open the SageMaker AI console at [https://console.amazonaws.cn/sagemaker/](https://console.amazonaws.cn/sagemaker/).
1. On the left navigation pane, choose **Admin configurations**.
1. Under **Admin configurations**, choose **Domains**.
1. From the list of domains, select the user profile’s domain.
1. On the **Domain details** page, select the **User profiles** tab.
1. Choose your **User profile**.
1. On the user profile's page, select the **App Configurations** tab.
1. In the **Canvas** section, choose **Edit**.
1. In the **ML Ops configuration** section, turn on the **Enable direct deployment of Canvas models** toggle to enable deployment permissions.
1. Choose **Submit** to save the changes to your domain settings.
The user profile should now have model deployment permissions.
After granting permissions to the domain or user profile, make sure that the user logs out of their Canvas application and logs back in to apply the permission changes.
## Deploy a model
To get started with deploying your model, you create a new deployment in Canvas and specify the model version that you want to deploy along with the ML infrastructure, such as the type and number of compute instances that you would like to use for hosting the model.
Canvas suggests a default type and number of instances based on your model type, or you can learn more about the various SageMaker AI instance types on the [Amazon SageMaker pricing page](https://www.amazonaws.cn/sagemaker/pricing/). You are charged based on the SageMaker AI instance pricing while your endpoint is active.
When deploying JumpStart foundation models, you also have the option to specify the length of the deployment time. You can deploy the model to an endpoint indefinitely (meaning the endpoint is active until you delete the deployment). Or, if you only need the endpoint for a short period of time and would like to reduce costs, you can deploy the model to an endpoint for a specified amount of time, after which SageMaker AI shuts down the endpoint for you.
**Note**
If you deploy a model for a specified amount of time, stay logged in to the Canvas application for the duration of the endpoint. If you log out of or delete the application, then Canvas is unable to shut down the endpoint at the specified time.
After your model is deployed to a SageMaker AI Hosting [real-time inference endpoint](https://docs.amazonaws.cn/sagemaker/latest/dg/realtime-endpoints.html), you can begin making predictions by *invoking* the endpoint.
There are several different ways for you to deploy a model from the Canvas application. You can access the model deployment option through any of the following methods:
+ On the **My models** page of the Canvas application, choose the model that you want to deploy. Then, from the model’s **Versions** page, choose the **More options** icon (![\[Vertical ellipsis icon representing a menu or more options.\]](http://docs.amazonaws.cn/en_us/sagemaker/latest/dg/images/studio/canvas/more-options-icon.png)) next to a model version and select **Deploy**.
+ When on the details page for a model version, on the **Analyze** tab, choose the **Deploy** option.
+ When on the details page for a model version, on the **Predict** tab, choose the **More options** icon (![\[Vertical ellipsis icon representing a menu or more options.\]](http://docs.amazonaws.cn/en_us/sagemaker/latest/dg/images/studio/canvas/more-options-icon.png)) at the top of the page and select **Deploy**.
+ On the **ML Ops** page of the Canvas application, choose the **Deployments** tab and then choose **Create deployment**.
+ For JumpStart foundation models and fine-tuned foundation models, go to the **Ready-to-use models** page of the Canvas application. Choose **Generate, extract and summarize content**. Then, find the JumpStart foundation model or fine-tuned foundation model that you want to deploy. Choose the model, and on the model's chat page, choose the **Deploy** button.
All of these methods open the **Deploy model** side panel, where you specify the deployment configuration for your model. To deploy the model from this panel, do the following:
1. (Optional) If you’re creating a deployment from the **ML Ops** page, you’ll have the option to **Select model and version**. Use the dropdown menus to select the model and model version that you want to deploy.
1. Enter a name in the **Deployment name** field.
1. (For JumpStart foundation models and fine-tuned foundation models only) Choose a **Deployment length**. Select **Indefinite** to leave the endpoint active until you shut it down, or select **Specify length** and then enter the period of time for which you want the endpoint to remain active.
1. For **Instance type**, SageMaker AI detects a default instance type and number that is suitable for your model. However, you can change the instance type that you would like to use for hosting your model.
**Note**
If you run out of the instance quota for the chosen instance type on your Amazon account, you can request a quota increase. For more information about the default quotas and how to request an increase, see [Amazon SageMaker AI endpoints and quotas](https://docs.amazonaws.cn/general/latest/gr/sagemaker.html) in the *Amazon General Reference guide*.
1. For **Instance count**, you can set the number of active instances that are used for your endpoint. SageMaker AI detects a default number that is suitable for your model, but you can change this number.
1. When you’re ready to deploy your model, choose **Deploy**.
Your model should now be deployed to an endpoint.
# View your deployments
You might want to check the status or details of a model deployment in Amazon SageMaker Canvas. For example, if your deployment failed, you might want to check the details to troubleshoot.
You can view your Canvas model deployments from the Canvas application or from the Amazon SageMaker AI console.
To view deployment details from Canvas, choose one of the following procedures:
To view your deployment details from the **ML Ops** page, do the following:
1. Open the SageMaker Canvas application.
1. In the left navigation pane, choose **ML Ops**.
1. Choose the **Deployments** tab.
1. Choose your deployment by name from the list.
To view your deployment details from a model version’s page, do the following:
1. In the SageMaker Canvas application, go to your model version’s details page.
1. Choose the **Deploy** tab.
1. On the **Deployments ** section that lists all of the deployment configurations associated with that model version, find your deployment.
1. Choose the **More options** icon (![\[More options icon for the output CSV file.\]](http://docs.amazonaws.cn/en_us/sagemaker/latest/dg/images/studio/canvas/more-options-icon.png)), and then select **View details** to open the details page.
The details page for your deployment opens, and you can view information such as the time of the most recent prediction, the endpoint’s status and configuration, and the model version that is currently deployed to the endpoint.
You can also view your currently active Canvas workspace instances and active endpoints from the **SageMaker AI dashboard** in the [SageMaker AI console](https://console.amazonaws.cn/sagemaker/). Your Canvas endpoints are listed alongside any other SageMaker AI Hosting endpoints that you’ve created, and you can filter them by searching for endpoints with the Canvas tag.
The following screenshot shows the SageMaker AI dashboard. In the **Canvas** section, you can see that one workspace instance is in service and four endpoints are active.
![\[Screenshot of the SageMaker AI dashboard showing the active Canvas workspace instances and endpoints.\]](http://docs.amazonaws.cn/en_us/sagemaker/latest/dg/images/studio/canvas/canvas-sagemaker-dashboard.png)
# Update a deployment configuration
You can update the deployment configuration for models that you've deployed to endpoints in Amazon SageMaker Canvas. For example, you can deploy an updated model version to the endpoint, or you can update the instance type or number of instances behind the endpoint based on your capacity needs.
There are several different ways for you to update your deployment from the Canvas application. You can use any of the following methods:
+ On the **ML Ops** page of the Canvas application, you can choose the **Deployments** tab and select the deployment that you want to update. Then, choose **Update configuration**.
+ When on the details page for a model version, on the **Deploy** tab, you can view the deployments for that version. Next to the deployment, choose the **More options** icon (![\[More options icon for the output CSV file.\]](http://docs.amazonaws.cn/en_us/sagemaker/latest/dg/images/studio/canvas/more-options-icon.png)) and then choose **Update configuration**.
Both of the preceding methods open the **Update configuration** side panel, where you can make changes to your deployment configuration. To update the configuration, do the following:
1. For the **Select version** dropdown menu, you can select a different model version to deploy to the endpoint.
**Note**
When updating a deployment configuration, you can only choose a different model version to deploy. To deploy a different model, create a new deployment.
1. For **Instance type**, you can select a different instance type for hosting your model.
1. For **Instance count**, you can change the number of active instances that are used for your endpoint.
1. Choose **Save**.
Your deployment configuration should now be updated.
# Test your deployment
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
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
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.
# Invoke your endpoint
**Note**
We recommend that you [test your model deployment in Amazon SageMaker Canvas](canvas-deploy-model-test.md) before invoking a SageMaker AI endpoint programmatically.
You can use your Amazon SageMaker Canvas models that you've deployed to a SageMaker AI endpoint in production with your applications. Invoke the endpoint programmatically the same way that you invoke any other [SageMaker AI real-time endpoint](https://docs.amazonaws.cn/sagemaker/latest/dg/realtime-endpoints.html). Invoking an endpoint programmatically returns a response object which contains the same fields described in [Test your deployment](canvas-deploy-model-test.md).
For more detailed information about how to programmatically invoke endpoints, see [Invoke models for real-time inference](realtime-endpoints-test-endpoints.md).
The following Python examples show you how to invoke your endpoint based on the model type.
## JumpStart foundation models
The following example shows you how to invoke a JumpStart foundation model that you've deployed to an endpoint.
```
import boto3
import pandas as pd
client = boto3.client("runtime.sagemaker")
body = pd.DataFrame(
[['feature_column1', 'feature_column2'],
['feature_column1', 'feature_column2']]
).to_csv(header=False, index=False).encode("utf-8")
response = client.invoke_endpoint(
EndpointName="endpoint_name",
ContentType="text/csv",
Body=body,
Accept="application/json"
)
```
## Numeric and categorical prediction models
The following example shows you how to invoke numeric or categorical prediction models.
```
import boto3
import pandas as pd
client = boto3.client("runtime.sagemaker")
body = pd.DataFrame(['feature_column1', 'feature_column2'], ['feature_column1', 'feature_column2']).to_csv(header=False, index=False).encode("utf-8")
response = client.invoke_endpoint(
EndpointName="endpoint_name",
ContentType="text/csv",
Body=body,
Accept="application/json"
)
```
## Time series forecasting models
The following example shows you how to invoke time series forecasting models. For a complete example of how to test invoke a time series forecasting model, see [ Time-Series Forecasting with Amazon SageMaker Autopilot](https://github.com/aws/amazon-sagemaker-examples/blob/eef13dae197a6e588a8bc111aba3244f99ee0fbb/autopilot/autopilot_time_series.ipynb).
```
import boto3
import pandas as pd
csv_path = './real-time-payload.csv'
data = pd.read_csv(csv_path)
client = boto3.client("runtime.sagemaker")
body = data.to_csv(index=False).encode("utf-8")
response = client.invoke_endpoint(
EndpointName="endpoint_name",
ContentType="text/csv",
Body=body,
Accept="application/json"
)
```
## Image prediction models
The following example shows you how to invoke image prediction models.
```
import boto3
client = boto3.client("runtime.sagemaker")
with open("example_image.jpg", "rb") as file:
body = file.read()
response = client.invoke_endpoint(
EndpointName="endpoint_name",
ContentType="application/x-image",
Body=body,
Accept="application/json"
)
```
## Text prediction models
The following example shows you how to invoke text prediction models.
```
import boto3
import pandas as pd
client = boto3.client("runtime.sagemaker")
body = pd.DataFrame([["Example text 1"], ["Example text 2"]]).to_csv(header=False, index=False).encode("utf-8")
response = client.invoke_endpoint(
EndpointName="endpoint_name",
ContentType="text/csv",
Body=body,
Accept="application/json"
)
```
# Delete a model deployment
You can delete your model deployments from the Amazon SageMaker Canvas application. This action also deletes the endpoint from the SageMaker AI console and shuts down any endpoint-related resources.
**Note**
Optionally, you can delete your endpoint through the [SageMaker AI console](https://console.amazonaws.cn/sagemaker/) or using the SageMaker AI `DeleteEndpoint` API. For more information, see [Delete Endpoints and Resources](realtime-endpoints-delete-resources.md). However, when you delete the endpoint through the SageMaker AI console or APIs instead of the Canvas application, the list of deployments in Canvas isn’t automatically updated. You must also delete the deployment from the Canvas application to remove it from the list.
To delete a deployment in Canvas, 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 that you want to delete.
1. At the top of the deployment details page, choose the **More options** icon (![\[More options icon for the output CSV file.\]](http://docs.amazonaws.cn/en_us/sagemaker/latest/dg/images/studio/canvas/more-options-icon.png)).
1. Choose **Delete deployment**.
1. In the ** Delete deployment** dialog box, choose **Delete**.
Your deployment and SageMaker AI Hosting endpoint should now be deleted from both Canvas and the SageMaker AI console.