Deploy and score a machine learning model by using an online endpoint

APPLIES TO: Azure CLI ml extension v2 (current) Python SDK azure-ai-ml v2 (current)

Learn how to use an online endpoint to deploy your model, so you don't have to create and manage the underlying infrastructure. You'll begin by deploying a model on your local machine to debug any errors, and then you'll deploy and test it in Azure.

You'll also learn how to view the logs and monitor the service-level agreement (SLA). You start with a model and end up with a scalable HTTPS/REST endpoint that you can use for online and real-time scoring.

Online endpoints are endpoints that are used for online (real-time) inferencing. There are two types of online endpoints: managed online endpoints and Kubernetes online endpoints. For more information on endpoints, and differences between managed online endpoints and Kubernetes online endpoints, see What are Azure Machine Learning endpoints?.

Managed online endpoints help to deploy your ML models in a turnkey manner. Managed online endpoints work with powerful CPU and GPU machines in Azure in a scalable, fully managed way. Managed online endpoints take care of serving, scaling, securing, and monitoring your models, freeing you from the overhead of setting up and managing the underlying infrastructure.

The main example in this doc uses managed online endpoints for deployment. To use Kubernetes instead, see the notes in this document inline with the managed online endpoint discussion.


To create managed online endpoints in the Azure Machine Learning studio, see Use managed online endpoints in the studio.


Before following the steps in this article, make sure you have the following prerequisites:

  • Azure role-based access controls (Azure RBAC) are used to grant access to operations in Azure Machine Learning. To perform the steps in this article, your user account must be assigned the owner or contributor role for the Azure Machine Learning workspace, or a custom role allowing Microsoft.MachineLearningServices/workspaces/onlineEndpoints/*. For more information, see Manage access to an Azure Machine Learning workspace.

  • If you haven't already set the defaults for the Azure CLI, save your default settings. To avoid passing in the values for your subscription, workspace, and resource group multiple times, run this code:

    az account set --subscription <subscription ID>
    az configure --defaults workspace=<Azure Machine Learning workspace name> group=<resource group>
  • (Optional) To deploy locally, you must install Docker Engine on your local computer. We highly recommend this option, so it's easier to debug issues.


The examples in this document assume that you are using the Bash shell. For example, from a Linux system or Windows Subsystem for Linux.

Prepare your system

Clone the sample repository

To follow along with this article, first clone the samples repository (azureml-examples). Then, run the following code to go to the samples directory:

git clone --depth 1
cd azureml-examples
cd cli


Use --depth 1 to clone only the latest commit to the repository, which reduces time to complete the operation.

Set an endpoint name

To set your endpoint name, run the following command (replace YOUR_ENDPOINT_NAME with a unique name).

For Unix, run this command:



Endpoint names must be unique within an Azure region. For example, in the Azure westus2 region, there can be only one endpoint with the name my-endpoint.

Define the endpoint and deployment

The following snippet shows the endpoints/online/managed/sample/endpoint.yml file:

name: my-endpoint
auth_mode: key


For a full description of the YAML, see Online endpoint YAML reference.

The reference for the endpoint YAML format is described in the following table. To learn how to specify these attributes, see the YAML example in Prepare your system or the online endpoint YAML reference. For information about limits related to managed endpoints, see Manage and increase quotas for resources with Azure Machine Learning.

Key Description
$schema (Optional) The YAML schema. To see all available options in the YAML file, you can view the schema in the preceding example in a browser.
name The name of the endpoint. It must be unique in the Azure region.
Naming rules are defined under managed online endpoint limits.
auth_mode Use key for key-based authentication. Use aml_token for Azure Machine Learning token-based authentication. key doesn't expire, but aml_token does expire. (Get the most recent token by using the az ml online-endpoint get-credentials command.)

The example contains all the files needed to deploy a model on an online endpoint. To deploy a model, you must have:

  • Model files (or the name and version of a model that's already registered in your workspace). In the example, we have a scikit-learn model that does regression.
  • The code that's required to score the model. In this case, we have a file.
  • An environment in which your model runs. As you'll see, the environment might be a Docker image with Conda dependencies, or it might be a Dockerfile.
  • Settings to specify the instance type and scaling capacity.

The following snippet shows the endpoints/online/managed/sample/blue-deployment.yml file, with all the required inputs:

name: blue
endpoint_name: my-endpoint
  path: ../../model-1/model/
  code: ../../model-1/onlinescoring/
  conda_file: ../../model-1/environment/conda.yml
instance_type: Standard_DS3_v2
instance_count: 1

The table describes the attributes of a deployment:

Key Description
name The name of the deployment.
model In this example, we specify the model properties inline: path. Model files are automatically uploaded and registered with an autogenerated name. For related best practices, see the tip in the next section.
code_configuration.code.path The directory on the local development environment that contains all the Python source code for scoring the model. You can use nested directories and packages.
code_configuration.scoring_script The Python file that's in the code_configuration.code.path scoring directory on the local development environment. This Python code must have an init() function and a run() function. The function init() will be called after the model is created or updated (you can use it to cache the model in memory, for example). The run() function is called at every invocation of the endpoint to do the actual scoring and prediction.
environment Contains the details of the environment to host the model and code. In this example, we have inline definitions that include thepath. We'll use environment.docker.image for the image. The conda_file dependencies will be installed on top of the image. For more information, see the tip in the next section.
instance_type The VM SKU that will host your deployment instances. For more information, see Managed online endpoints supported VM SKUs.
instance_count The number of instances in the deployment. Base the value on the workload you expect. For high availability, we recommend that you set instance_count to at least 3. We reserve an extra 20% for performing upgrades. For more information, see managed online endpoint quotas.

During deployment, the local files such as the Python source for the scoring model, are uploaded from the development environment.

For more information about the YAML schema, see the online endpoint YAML reference.


To use Kubernetes instead of managed endpoints as a compute target:

  1. Create and attach your Kubernetes cluster as a compute target to your Azure Machine Learning workspace by using Azure Machine Learning studio.
  2. Use the endpoint YAML to target Kubernetes instead of the managed endpoint YAML. You'll need to edit the YAML to change the value of target to the name of your registered compute target. You can use this deployment.yaml that has additional properties applicable to Kubernetes deployment.

All the commands that are used in this article (except the optional SLA monitoring and Azure Log Analytics integration) can be used either with managed endpoints or with Kubernetes endpoints.

Register your model and environment separately

In this example, we specify the path (where to upload files from) inline. The CLI automatically uploads the files and registers the model and environment. As a best practice for production, you should register the model and environment and specify the registered name and version separately in the YAML. Use the form model: azureml:my-model:1 or environment: azureml:my-env:1.

For registration, you can extract the YAML definitions of model and environment into separate YAML files and use the commands az ml model create and az ml environment create. To learn more about these commands, run az ml model create -h and az ml environment create -h.

Use different CPU and GPU instance types

The preceding YAML uses a general-purpose type (Standard_DS2_v2) and a non-GPU Docker image (in the YAML, see the image attribute). For GPU compute, choose a GPU compute type SKU and a GPU Docker image.

For supported general-purpose and GPU instance types, see Managed online endpoints supported VM SKUs. For a list of Azure Machine Learning CPU and GPU base images, see Azure Machine Learning base images.


To use Kubernetes instead of managed endpoints as a compute target, see Introduction to Kubernetes compute target

Use more than one model

Currently, you can specify only one model per deployment in the YAML. If you have more than one model, when you register the model, copy all the models as files or subdirectories into a folder that you use for registration. In your scoring script, use the environment variable AZUREML_MODEL_DIR to get the path to the model root folder. The underlying directory structure is retained. For an example of deploying multiple models to one deployment, see Deploy multiple models to one deployment.


If you have more than 1500 files to register, you may consider compressing the files or subdirectories as .tar.gz when registering the model. To consume the models, you can uncompress the files or subdirectories in the init() function from the scoring script. Alternatively, when you register the model, set the azureml.unpack property to True, which will allow automatic uncompression. In either case, uncompression happens once in the initialization stage.

Understand the scoring script


The format of the scoring script for online endpoints is the same format that's used in the preceding version of the CLI and in the Python SDK.

As noted earlier, the script specified in code_configuration.scoring_script must have an init() function and a run() function.

This example uses the file:

import os
import logging
import json
import numpy
import joblib

def init():
    This function is called when the container is initialized/started, typically after create/update of the deployment.
    You can write the logic here to perform init operations like caching the model in memory
    global model
    # AZUREML_MODEL_DIR is an environment variable created during deployment.
    # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)
    # Please provide your model's folder name if there is one
    model_path = os.path.join(
        os.getenv("AZUREML_MODEL_DIR"), "model/sklearn_regression_model.pkl"
    # deserialize the model file back into a sklearn model
    model = joblib.load(model_path)"Init complete")

def run(raw_data):
    This function is called for every invocation of the endpoint to perform the actual scoring/prediction.
    In the example we extract the data from the json input and call the scikit-learn model's predict()
    method and return the result back
    """"model 1: request received")
    data = json.loads(raw_data)["data"]
    data = numpy.array(data)
    result = model.predict(data)"Request processed")
    return result.tolist()

The init() function is called when the container is initialized or started. Initialization typically occurs shortly after the deployment is created or updated. Write logic here for global initialization operations like caching the model in memory (as we do in this example). The run() function is called for every invocation of the endpoint and should do the actual scoring and prediction. In the example, we extract the data from the JSON input, call the scikit-learn model's predict() method, and then return the result.

Deploy and debug locally by using local endpoints

To save time debugging, we highly recommend that you test-run your endpoint locally. For more, see Debug online endpoints locally in Visual Studio Code.



The goal of a local endpoint deployment is to validate and debug your code and configuration before you deploy to Azure. Local deployment has the following limitations:

  • Local endpoints do not support traffic rules, authentication, or probe settings.
  • Local endpoints support only one deployment per endpoint.
  • Local endpoints do not support registered models. To use models already registered, you can download them using CLI or SDK and refer to them in the deployment definition.


You can use Azure Machine Learning inference HTTP server Python package to debug your scoring script locally without Docker Engine. Debugging with the inference server helps you to debug the scoring script before deploying to local endpoints so that you can debug without being affected by the deployment container configurations.

Deploy the model locally

First create an endpoint. Optionally, for a local endpoint, you can skip this step and directly create the deployment (next step), which will, in turn, create the required metadata. Deploying models locally is useful for development and testing purposes.

az ml online-endpoint create --local -n $ENDPOINT_NAME -f endpoints/online/managed/sample/endpoint.yml

Now, create a deployment named blue under the endpoint.

az ml online-deployment create --local -n blue --endpoint $ENDPOINT_NAME -f endpoints/online/managed/sample/blue-deployment.yml

The --local flag directs the CLI to deploy the endpoint in the Docker environment.


Use Visual Studio Code to test and debug your endpoints locally. For more information, see debug online endpoints locally in Visual Studio Code.

Verify the local deployment succeeded

Check the status to see whether the model was deployed without error:

az ml online-endpoint show -n $ENDPOINT_NAME --local

The output should appear similar to the following JSON. The provisioning_state is Succeeded.

  "auth_mode": "key",
  "location": "local",
  "name": "docs-endpoint",
  "properties": {},
  "provisioning_state": "Succeeded",
  "scoring_uri": "http://localhost:49158/score",
  "tags": {},
  "traffic": {}

The following table contains the possible values for provisioning_state:

State Description
Creating The resource is being created.
Updating The resource is being updated.
Deleting The resource is being deleted.
Succeeded The create/update operation was successful.
Failed The create/update/delete operation has failed.

Invoke the local endpoint to score data by using your model

Invoke the endpoint to score the model by using the convenience command invoke and passing query parameters that are stored in a JSON file:

az ml online-endpoint invoke --local --name $ENDPOINT_NAME --request-file endpoints/online/model-1/sample-request.json

If you want to use a REST client (like curl), you must have the scoring URI. To get the scoring URI, run az ml online-endpoint show --local -n $ENDPOINT_NAME. In the returned data, find the scoring_uri attribute. Sample curl based commands are available later in this doc.

Review the logs for output from the invoke operation

In the example file, the run() method logs some output to the console.

You can view this output by using the get-logs command:

az ml online-deployment get-logs --local -n blue --endpoint $ENDPOINT_NAME

Deploy your online endpoint to Azure

Next, deploy your online endpoint to Azure.

Deploy to Azure

To create the endpoint in the cloud, run the following code:

az ml online-endpoint create --name $ENDPOINT_NAME -f endpoints/online/managed/sample/endpoint.yml

To create the deployment named blue under the endpoint, run the following code:

az ml online-deployment create --name blue --endpoint $ENDPOINT_NAME -f endpoints/online/managed/sample/blue-deployment.yml --all-traffic

This deployment might take up to 15 minutes, depending on whether the underlying environment or image is being built for the first time. Subsequent deployments that use the same environment will finish processing more quickly.


  • If you prefer not to block your CLI console, you may add the flag --no-wait to the command. However, this will stop the interactive display of the deployment status.


The --all-traffic flag in the above az ml online-deployment create allocates 100% of the traffic to the endpoint to the newly created deployment. Though this is helpful for development and testing purposes, for production, you might want to open traffic to the new deployment through an explicit command. For example, az ml online-endpoint update -n $ENDPOINT_NAME --traffic "blue=100"

Check the status of the endpoint

The show command contains information in provisioning_status for endpoint and deployment:

az ml online-endpoint show -n $ENDPOINT_NAME

You can list all the endpoints in the workspace in a table format by using the list command:

az ml online-endpoint list --output table

Check the status of the online deployment

Check the logs to see whether the model was deployed without error:

az ml online-deployment get-logs --name blue --endpoint $ENDPOINT_NAME

By default, logs are pulled from inference-server. To see the logs from storage-initializer (it mounts assets like model and code to the container), add the --container storage-initializer flag.

For more information on deployment logs, see Get container logs.

Invoke the endpoint to score data by using your model

You can use either the invoke command or a REST client of your choice to invoke the endpoint and score some data:

az ml online-endpoint invoke --name $ENDPOINT_NAME --request-file endpoints/online/model-1/sample-request.json

The following example shows how to get the key used to authenticate to the endpoint:


You can control which Azure Active Directory security principals can get the authentication key by assigning them to a custom role that allows Microsoft.MachineLearningServices/workspaces/onlineEndpoints/token/action and Microsoft.MachineLearningServices/workspaces/onlineEndpoints/listkeys/action. For more information, see Manage access to an Azure Machine Learning workspace.

ENDPOINT_KEY=$(az ml online-endpoint get-credentials -n $ENDPOINT_NAME -o tsv --query primaryKey)

Next, use curl to score data.

SCORING_URI=$(az ml online-endpoint show -n $ENDPOINT_NAME -o tsv --query scoring_uri)

curl --request POST "$SCORING_URI" --header "Authorization: Bearer $ENDPOINT_KEY" --header 'Content-Type: application/json' --data @endpoints/online/model-1/sample-request.json

Notice we use show and get-credentials commands to get the authentication credentials. Also notice that we're using the --query flag to filter attributes to only what we need. To learn more about --query, see Query Azure CLI command output.

To see the invocation logs, run get-logs again.

For information on authenticating using a token, see Authenticate to online endpoints.

(Optional) Update the deployment

If you want to update the code, model, or environment, update the YAML file, and then run the az ml online-endpoint update command.


If you update instance count and along with other model settings (code, model, or environment) in a single update command: first the scaling operation will be performed, then the other updates will be applied. In production environment is a good practice to perform these operations separately.

To understand how update works:

  1. Open the file online/model-1/onlinescoring/

  2. Change the last line of the init() function: After"Init complete"), add"Updated successfully").

  3. Save the file.

  4. Run this command:

    az ml online-deployment update -n blue --endpoint $ENDPOINT_NAME -f endpoints/online/managed/sample/blue-deployment.yml


    Updating by using YAML is declarative. That is, changes in the YAML are reflected in the underlying Azure Resource Manager resources (endpoints and deployments). A declarative approach facilitates GitOps: All changes to endpoints and deployments (even instance_count) go through the YAML.


    With the update command, you can use the --set parameter in the Azure CLI to override attributes in your YAML or to set specific attributes without passing the YAML file. Using --set for single attributes is especially valuable in development and test scenarios. For example, to scale up the instance_count value for the first deployment, you could use the --set instance_count=2 flag. However, because the YAML isn't updated, this technique doesn't facilitate GitOps.

  5. Because you modified the init() function (init() runs when the endpoint is created or updated), the message Updated successfully will be in the logs. Retrieve the logs by running:

    az ml online-deployment get-logs --name blue --endpoint $ENDPOINT_NAME

The update command also works with local deployments. Use the same az ml online-deployment update command with the --local flag.


The above is an example of inplace rolling update.

  • For managed online endpoint, the same deployment is updated with the new configuration, with 20% nodes at a time, i.e. if the deployment has 10 nodes, 2 nodes at a time will be updated.
  • For Kubernetes online endpoint, the system will iterately create a new deployment instance with the new configuration and delete the old one.
  • For production usage, you might want to consider blue-green deployment, which offers a safer alternative.

(Optional) Configure autoscaling

Autoscale automatically runs the right amount of resources to handle the load on your application. Managed online endpoints support autoscaling through integration with the Azure monitor autoscale feature. To configure autoscaling, see How to autoscale online endpoints.

(Optional) Monitor SLA by using Azure Monitor

To view metrics and set alerts based on your SLA, complete the steps that are described in Monitor online endpoints.

(Optional) Integrate with Log Analytics

The get-logs command for CLI or the get_logs method for SDK provides only the last few hundred lines of logs from an automatically selected instance. However, Log Analytics provides a way to durably store and analyze logs. For more information on using logging, see Monitor online endpoints

How to configure emails in the studio

To start receiving emails when your job, online endpoint, or batch endpoint is complete or if there's an issue (failed, canceled), use the following steps:

  1. In Azure ML studio, go to settings by selecting the gear icon.
  2. Select the Email notifications tab.
  3. Toggle to enable or disable email notifications for a specific event.

Screenshot of Azure ML studio's settings on the email notifications tab.

Delete the endpoint and the deployment

If you aren't going use the deployment, you should delete it by running the following code (it deletes the endpoint and all the underlying deployments):

az ml online-endpoint delete --name $ENDPOINT_NAME --yes --no-wait

Next steps

Try safe rollout of your models as a next step:

To learn more, review these articles: