Mock sample for your project: StorageImportExport API

Network operation results.

APIs for managing software update configurations.

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 Resource Health Client.

Edge gateway pool operation endpoints and objects.

Azure Application Insights workbook template type.

Resource provider health operation endpoints and objects.

APIs to manage web application firewall rules.

Describes the interface for the per-tenant enclave service.

Azure Application Insights client for web test based alerting.

An API for face detection, verification, and identification.

The FinSpace management service provides the APIs for managing the FinSpace environments.

AWS IoT Greengrass seamlessly extends AWS onto physical devices so they can act locally on the data they generate, while still using the cloud for management, analytics, and durable storage. AWS IoT Greengrass ensures your devices can respond quickly to local events and operate with intermittent connectivity. AWS IoT Greengrass minimizes the cost of transmitting data to the cloud by allowing you to author AWS Lambda functions that execute locally.

Network operation results.

Provides APIs for creating and managing Amazon SageMaker resources. Other Resources: Amazon SageMaker Developer Guide Amazon Augmented AI Runtime API Reference

Contains all data plane API operations and data types for the Amazon SageMaker Feature Store. Use this API to put, delete, and retrieve (get) features from a feature store. Use the following operations to configure your OnlineStore and OfflineStore features, and to create and manage feature groups: CreateFeatureGroup DeleteFeatureGroup DescribeFeatureGroup ListFeatureGroups

AWS Data Pipeline configures and manages a data-driven workflow called a pipeline. AWS Data Pipeline handles the details of scheduling and ensuring that data dependencies are met so that your application can focus on processing the data. AWS Data Pipeline provides a JAR implementation of a task runner called AWS Data Pipeline Task Runner. AWS Data Pipeline Task Runner provides logic for common data management scenarios, such as performing database queries and running data analysis using Amazon Elastic MapReduce (Amazon EMR). You can use AWS Data Pipeline Task Runner as your task runner, or you can write your own task runner to provide custom data management. AWS Data Pipeline implements two main sets of functionality. Use the first set to create a pipeline and define data sources, schedules, dependencies, and the transforms to be performed on the data. Use the second set in your task runner application to receive the next task ready for processing. The logic for performing the task, such as querying the data, running data analysis, or converting the data from one format to another, is contained within the task runner. The task runner performs the task assigned to it by the web service, reporting progress to the web service as it does so. When the task is done, the task runner reports the final success or failure of the task to the web service.

Using the Amazon Cognito User Pools API, you can create a user pool to manage directories and users. You can authenticate a user to obtain tokens related to user identity and access policies. This API reference provides information about user pools in Amazon Cognito User Pools. For more information, see the Amazon Cognito Documentation.

Azure Active Directory Client.

The Admin Subscriptions Management Client.