Mock sample for your project: DataLakeStoreFileSystemManagementClient API

The Container Service Client.

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.

Time Series Insights environment data plane client for PAYG (Preview L1 SKU) environments.

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

Azure NotificationHub client

Microsoft Azure Quota Resource Provider.

This API describe Azure Reservation

The Microsoft Azure management API provides create, read, update, and delete functionality for Azure PostgreSQL resources including servers, databases, firewall rules, VNET rules, security alert policies, log files and configurations with new business model.

This Swagger was generated by the API Framework.

API to manage VM Insights Onboarding

APIs to deploy and manage resources to SeaBreeze.

Dev Spaces REST API

The Compute Management Client.

Provides create, read, update and delete functionality for Azure SQL Database resources including recommendations and operations.

HyperDrive REST API

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.

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.

Azure DevOps Resource Provider