Mock sample for your project: SearchServiceClient API

Subscription client provides an interface to create and manage Azure subscriptions programmatically.

REST APIs for Azure Active Directory Connect Health

The Admin Gallery Management Client.

The HDInsight Management Client.

The Admin Subscriptions Management Client.

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

The Resource Health Client.

The service for managing third party addons.

The Admin Subscriptions Management Client.

The Microsoft Azure management API provides create, read, update, and delete functionality for Azure MariaDB resources including servers, databases, firewall rules, VNET rules, security alert policies, log files, encryption keys, active directory administrator and configurations.

A client for issuing REST requests to the Azure Batch service.

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

The Admin Gallery Management Client.

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

APIs for managing software update configurations.

Azure Digital Twins Client for managing DigitalTwinsInstance

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.