Azure Active Directory Client.

The AzureData management API provides a RESTful set of web APIs to manage Azure Data Resources. For example, register, delete and retrieve a SQL Server, SQL Server registration.

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

The Azure BatchAI Management API.

Billing client provides access to billing resources for Azure subscriptions.

REST API for Azure Blockchain Service

Azure Blueprints Client provides access to blueprint definitions, assignments, and artifacts, and related blueprint operations.

Azure Blueprints Client provides access to blueprint definitions, assignments, and artifacts, and related blueprint operations.

Azure Blueprints Client provides access to blueprint definitions, assignments, and artifacts, and related blueprint operations.

Azure Bot Service is a platform for creating smart conversational agents.

Use these APIs to manage Azure CDN resources through the Azure Resource Manager. You must make sure that requests made to these resources are secure.

APIs to manage web application firewall rules for Azure CDN

Cognitive Services Management Client

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 Anomaly Finder API detects anomalies automatically in time series data. It supports two functionalities, one is for detecting the whole series with model trained by the timeseries, another is detecting last point with model trained by points before. By using this service, business customers can discover incidents and establish a logic flow for root cause analysis.