Mock sample for your project: Microsoft Insights API

Provides read, delete, and failover functionality for Azure SQL Database replication links.

The Microsoft Azure Network management API provides a RESTful set of web services that interact with Microsoft Azure Networks service to manage your network resources. The API has entities that capture the relationship between an end user and the Microsoft Azure Networks service.

Azure Resource Graph Query API Reference

Azure Log Analytics API reference

Azure NotificationHub client

This Swagger was generated by the API Framework.

PowerBI Dedicated Web API provides a RESTful set of web services that enables users to create, retrieve, update, and delete Power BI dedicated capacities

Assess your workloads for Azure.

REST API for Azure Redis Cache Service.

Provides read functionality for Azure SQL Database Backups

The Microsoft Azure Network management API provides a RESTful set of web services that interact with Microsoft Azure Networks service to manage your network resources. The API has entities that capture the relationship between an end user and the Microsoft Azure Networks service.

This Swagger was generated by the API Framework.

The Hybrid Compute Management Client.

Resource Provider

REST APIs for orchestrating deployments using the Azure Deployment Manager (ADM). See https://docs.microsoft.com/en-us/azure/azure-resource-manager/deployment-manager-overview for more information.

The Run Commands 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 Container Service Client.