Mock sample for your project: Azure Activity Log Alerts API

A Storage Cache provides scalable caching service for NAS clients, serving data from either NFSv3 or Blob at-rest storage (referred to as "Storage Targets"). These operations allow you to manage Caches.

REST API for Azure Blockchain Service

The Admin Gallery Management Client.

Specification for ManagedServices.

Role based access control provides you a way to apply granular level policy administration down to individual resources or resource groups. These calls handle provider operations.

REST APIs for Azure Advisor

Use this REST API to get all the issues across an Azure Api Management service.

An API for face detection, verification, and identification.

Use these REST APIs for performing operations on OpenId Connect Provider entity associated with your Azure API Management deployment. API Management allows you to access APIs secured with token from OpenID Connect Provider to be accessed from the Developer Console.

The Run Commands Client.

The HDInsight Management Client.

APIs to manage web application firewall rules for Azure CDN

The HDInsight Management Client.

The Compute Management Client.

Creates an Azure Data Lake Store filesystem client.

API spec for Microsoft.Security (Azure Security Center) resource provider

APIs for workload monitoring

This API describe ACE Provisioning ManagementPartner

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.