Mock sample for your project: Azure Machine Learning Model Management Service API

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.

ARM Databricks

The Admin Gallery Management Client.

Dev Spaces REST API

The Admin Storage Management Client.

Azure Log Analytics API reference for management of saved Queries within Query Packs.

The service for managing third party addons.

The Admin Compute Disk Management Client.

Billing client provides access to billing resources for Azure subscriptions.

The Azure Data Catalog Resource Provider Services API.

The service for managing third party addons.

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

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 Admin Storage Management Client.

You use the API to scan your content as it is generated. Content Moderator then processes your content and sends the results along with relevant information either back to your systems or to the built-in review tool. You can use this information to take decisions e.g. take it down, send to human judge, etc.
When using the API, images need to have a minimum of 128 pixels and a maximum file size of 4MB.
Text can be at most 1024 characters long.
If the content passed to the text API or the image API exceeds the size limits, the API will return an error code that informs about the issue.

Describes the interface for the per-tenant enclave service.

Creates an Azure Data Lake Analytics catalog client.

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

AWS Elemental MediaConvert

AWS Well-Architected Tool This is the AWS Well-Architected Tool API Reference. The AWS Well-Architected Tool API provides programmatic access to the AWS Well-Architected Tool in the AWS Management Console. For information about the AWS Well-Architected Tool, see the AWS Well-Architected Tool User Guide.

Mixed Reality Resource Provider Remote Rendering Resource API