Mock sample for your project: SqlVirtualMachineManagementClient API

The Microsoft Azure management API provides create, read, update, and delete functionality for Azure MySQL resources including servers, databases, firewall rules, VNET rules, log files and configurations with new business model.

Azure DevOps Resource Provider

The HDInsight Management Client.

The key vault client performs cryptographic key operations and vault operations against the Key Vault service.

Load balancer admin operation endpoints and objects.

Use these REST APIs for performing operations on entities like API, Product, and Subscription associated with your Azure API Management deployment.

Logical subnet operation endpoints and objects.

The HDInsight Management Client.

AzureBridge Admin Client.

The Azure SQL Server management API provides a RESTful set of web services that interact with Azure SQL Server services to manage your databases. The API enables users update server connection policy.

Microsoft.Intune Resource provider Api features in the swagger-2.0 specification

The HDInsight Management Client.

The Container Service Client.

An API for QnAMaker Service

An API for face detection, verification, and identification.

The Anomaly Detector API detects anomalies automatically in time series data. It supports two kinds of mode, one is for stateless using, another is for stateful using. In stateless mode, there are three functionalities. Entire Detect is for detecting the whole series with model trained by the time series, Last Detect is detecting last point with model trained by points before. ChangePoint Detect is for detecting trend changes in time series. In stateful mode, user can store time series, the stored time series will be used for detection anomalies. Under this mode, user can still use the above three functionalities by only giving a time range without preparing time series in client side. Besides the above three functionalities, stateful model also provide group based detection and labeling service. By leveraging labeling service user can provide labels for each detection result, these labels will be used for retuning or regenerating detection models. Inconsistency detection is a kind of group based detection, this detection will find inconsistency ones in a set of time series. By using anomaly detector service, business customers can discover incidents and establish a logic flow for root cause analysis.

The Container Service Client.