Mock sample for your project: Security Center API

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

Client that can be used to manage Azure Cognitive Search services and API keys.

The HDInsight Management Client.

The Azure management API provides a RESTful set of web services that interact with Azure Dedicated HSM RP.

The HDInsight Management Client.

Deployment Admin Client.

Role based access control provides you a way to apply granular level policy administration down to individual resources or resource groups. These operations enable you to manage role definitions and role assignments. A role definition describes the set of actions that can be performed on resources. A role assignment grants access to Azure Active Directory users.

The Azure Management Groups API enables consolidation of multiple subscriptions/resources into an organizational hierarchy and centrally manage access control, policies, alerting and reporting for those resources.

Azure IoT Central is a service that makes it easy to connect, monitor, and manage your IoT devices at scale.

Use these REST APIs for managing OAuth2 servers configuration in your Azure API Management deployment. OAuth 2.0 can be used to authorize developer accounts for Azure API Management. For more information refer to How to OAuth2.

The SAP HANA on Azure Management Client.

Creates an Azure Data Lake Store account management client.

Virtual Network admin operation endpoints and objects.

AWS Elemental MediaPackage VOD

Elastic Load Balancing A load balancer distributes incoming traffic across targets, such as your EC2 instances. This enables you to increase the availability of your application. The load balancer also monitors the health of its registered targets and ensures that it routes traffic only to healthy targets. You configure your load balancer to accept incoming traffic by specifying one or more listeners, which are configured with a protocol and port number for connections from clients to the load balancer. You configure a target group with a protocol and port number for connections from the load balancer to the targets, and with health check settings to be used when checking the health status of the targets. Elastic Load Balancing supports the following types of load balancers: Application Load Balancers, Network Load Balancers, Gateway Load Balancers, and Classic Load Balancers. This reference covers the following load balancer types: Application Load Balancer - Operates at the application layer (layer 7) and supports HTTP and HTTPS. Network Load Balancer - Operates at the transport layer (layer 4) and supports TCP, TLS, and UDP. Gateway Load Balancer - Operates at the network layer (layer 3). For more information, see the Elastic Load Balancing User Guide. All Elastic Load Balancing operations are idempotent, which means that they complete at most one time. If you repeat an operation, it succeeds.

Amazon Personalize is a machine learning service that makes it easy to add individualized recommendations to customers.

An API for face detection, verification, and identification.

Use these APIs to manage Visual Studio Team Services resources through the Azure Resource Manager. All task operations conform to the HTTP/1.1 protocol specification and each operation returns an x-ms-request-id header that can be used to obtain information about the request. You must make sure that requests made to these resources are secure. For more information, see https://docs.microsoft.com/en-us/rest/api/index.

AzureBridge Admin Client.

Logical subnet operation endpoints and objects.

Azure Log Analytics API reference