Mock sample for your project: CognitiveServicesManagementClient API

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

REST APIs for Azure Advisor

Deployment Admin Client.

IP pool operation endpoints and objects.

Use these REST APIs for performing operations on Quota entity associated with your Azure API Management deployment. To configure call rate limit and quota policies refer to how to configure call rate limit and quota.

Use these REST APIs for performing retrieving a collection of policy snippets available in Azure API Management deployment.

Use these REST APIs for managing OAuth2 servers configuration in your Azure API Management deployment. OAuth 2.0 can be used to authorize developer accounts for Azure API Management. For more information refer to How to OAuth2.

The Admin Gallery Management Client.

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 logger entity Azure API Management deployment.The Logger entity in API Management represents an event sink that you can use to log API Management events. Currently the Logger entity supports logging API Management events to Azure EventHub.

Use these REST APIs for performing operations on NamedValue entity associated with your Azure API Management deployment. API Management policies are a powerful capability of the system that allow the publisher to change the behavior of the API through configuration. Policies are a collection of statements that are executed sequentially on the request or response of an API. Policy statements can be constructed using literal text values, policy expressions, and NamedValues. Each API Management service instance has a NamedValues collection of key/value pairs that are global to the service instance. These NamedValues can be used to manage constant string values across all API configuration and policies.

The Admin KeyVault Management Client.

The User Subscription Management Client.

AzureBridge Admin Client.

This is the Amazon Lookout for Vision API Reference. It provides descriptions of actions, data types, common parameters, and common errors. Amazon Lookout for Vision enables you to find visual defects in industrial products, accurately and at scale. It uses computer vision to identify missing components in an industrial product, damage to vehicles or structures, irregularities in production lines, and even minuscule defects in silicon wafers — or any other physical item where quality is important such as a missing capacitor on printed circuit boards.

The AWS Migration Hub API methods help to obtain server and application migration status and integrate your resource-specific migration tool by providing a programmatic interface to Migration Hub. Remember that you must set your AWS Migration Hub home region before you call any of these APIs, or a HomeRegionNotSetException error will be returned. Also, you must make the API calls while in your home region.

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.

These APIs allow end users to operate on Azure Machine Learning Team Account resources. They support CRUD operations for Azure Machine Learning Team Accounts.

Creates a Microsoft.DataShare 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.