Mock sample for your project: BlueprintClient API

Virtual Machine Image Template

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

Use these REST APIs for performing operations on Backend entity in Azure API Management deployment. The Backend entity in API Management represents a backend service that is configured to skip certification chain validation when using a self-signed certificate to test mutual certificate authentication.

Use these REST APIs for performing operations on who is going to receive notifications associated with your Azure API Management deployment.

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.

Deployment Admin Client.

Storage system operation endpoints and objects.

Edge gateway pool operation endpoints and objects.

Use these REST APIs for performing operations on Identity Provider entity associated with your Azure API Management deployment. Setting up an external Identity Provider for authentication can help you manage the developer portal logins using the OAuth2 flow.

The service for managing third party addons.

Public IP Address admin endpoints and objects.

These APIs allow end users to manage Azure Machine Learning Models, Images, Profiles, and Services.

The Azure SQL Server management API provides a RESTful set of web services that interact with Azure SQL Server services to manage your databases. The API enables users update server connection policy.

Creates an Azure Data Lake Analytics job client.

Azure Maintenance Management Client

These APIs allow end users to operate on Azure Machine Learning Workspace resources.

Resource Provider

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.

These APIs allow end users to operate on Azure Machine Learning Commitment Plans resources and their child Commitment Association resources. They support CRUD operations for commitment plans, get and list operations for commitment associations, moving commitment associations between commitment plans, and retrieving commitment plan usage history.