This package contains an isomorphic SDK (runs both in Node.js and in browsers) for Azure ML Web Services Management client.

These APIs allow end users to operate on Azure Machine Learning Web Services resources. They support the following operations:

• Create or update a web service
• Get a web service
• Patch a web service
• Delete a web service
• Get All Web Services in a Resource Group
• Get All Web Services in a Subscription
• Get Web Services Keys

Source code | Package (NPM) | API reference documentation | Samples

• LTS versions of Node.js
• Latest versions of Safari, Chrome, Edge and Firefox.

See our support policy for more details.

• An Azure subscription.

Install the Azure ML Web Services Management client library for JavaScript with npm:

npm install @azure/arm-webservices

To create a client object to access the Azure ML Web Services Management API, you will need the endpoint of your Azure ML Web Services Management resource and a credential. The Azure ML Web Services Management client can use Azure Active Directory credentials to authenticate. You can find the endpoint for your Azure ML Web Services Management resource in the Azure Portal.

You can authenticate with Azure Active Directory using a credential from the @azure/identity library or an existing AAD Token.

To use the DefaultAzureCredential provider shown below, or other credential providers provided with the Azure SDK, please install the @azure/identity package:

npm install @azure/identity

You will also need to register a new AAD application and grant access to Azure ML Web Services Management by assigning the suitable role to your service principal (note: roles such as "Owner" will not grant the necessary permissions). Set the values of the client ID, tenant ID, and client secret of the AAD application as environment variables: AZURE_CLIENT_ID, AZURE_TENANT_ID, AZURE_CLIENT_SECRET.

For more information about how to create an Azure AD Application check out this guide.

Using Node.js and Node-like environments, you can use the DefaultAzureCredential class to authenticate the client.

import { AzureMLWebServicesManagementClient } from "@azure/arm-webservices";
import { DefaultAzureCredential } from "@azure/identity";

const subscriptionId = "00000000-0000-0000-0000-000000000000";
const client = new AzureMLWebServicesManagementClient(new DefaultAzureCredential(), subscriptionId);

For browser environments, use the InteractiveBrowserCredential from the @azure/identity package to authenticate.

import { InteractiveBrowserCredential } from "@azure/identity";
import { AzureMLWebServicesManagementClient } from "@azure/arm-webservices";

const subscriptionId = "00000000-0000-0000-0000-000000000000";
const credential = new InteractiveBrowserCredential({
  tenantId: "<YOUR_TENANT_ID>",
  clientId: "<YOUR_CLIENT_ID>",
});
const client = new AzureMLWebServicesManagementClient(credential, subscriptionId);

To use this client library in the browser, first you need to use a bundler. For details on how to do this, please refer to our bundling documentation.

AzureMLWebServicesManagementClient is the primary interface for developers using the Azure ML Web Services Management client library. Explore the methods on this client object to understand the different features of the Azure ML Web Services Management service that you can access.

Enabling logging may help uncover useful information about failures. In order to see a log of HTTP requests and responses, set the AZURE_LOG_LEVEL environment variable to info. Alternatively, logging can be enabled at runtime by calling setLogLevel in the @azure/logger:

import { setLogLevel } from "@azure/logger";

setLogLevel("info");

For more detailed instructions on how to enable logs, you can look at the @azure/logger package docs.

Please take a look at the samples directory for detailed examples on how to use this library.

If you'd like to contribute to this library, please read the contributing guide to learn more about how to build and test the code.

• Microsoft Azure SDK for JavaScript