Mock sample for your project: HDInsightManagementClient API

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

AzureBridge Admin Client.

Azure Application Insights client for ProactiveDetection configurations of a component.

The service is used to perform ink layout and recognition of written words and shapes. Ink strokes passed to the service are recognized and organized into recognition results in the response

Azure Application Insights client for web test based alerting.

An API for face detection, verification, and identification.

Use these REST APIs for performing operations on the ApiVersionSet entity associated with your Azure API Management deployment. Using this entity you create and manage API Version Sets that are used to group APIs for consistent versioning.

The Container Service Client.

The User Subscription Management Client.

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.

The Admin Compute Disk Management Client.

API spec for Microsoft.SecurityInsights (Azure Security Insights) resource provider

The Azure SQL Database management API provides a RESTful set of web APIs that interact with Azure SQL Database services to manage your databases. The API enables users to create, retrieve, update, and delete databases, servers, and other entities.

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

The Container Service Client.

ARM Databricks

Azure Service Fabric Resource Provider API Client

Provides create, read and update functionality for database Threat Detection policies.

This Swagger was generated by the API Framework.

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.