Mock sample for your project: DeploymentAdminClient API

Azure Feature Exposure Control (AFEC) provides a mechanism for the resource providers to control feature exposure to users. Resource providers typically use this mechanism to provide public/private preview for new features prior to making them generally available. Users need to explicitly register for AFEC features to get access to such functionality.

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

An API for QnAMaker Service

Deployment Admin Client.

Role based access control provides you a way to apply granular level policy administration down to individual resources or resource groups. These operations allow you to manage role definitions. A role definition describes the set of actions that can be performed on resources.

The Container Service Client.

The Admin Storage Management Client.

Azure Application Insights client for work item configurations for a component.

Use these REST APIs for performing operations on User entity in Azure API Management deployment. The User entity in API Management represents the developers that call the APIs of the products to which they are subscribed.

Azure Blueprints Client provides access to blueprint definitions, assignments, and artifacts, and related blueprint operations.

The Azure management API provides a RESTful set of web services that interact with Azure Key Vault.

AzureBridge Admin Client.

Amazon CloudWatch monitors your Amazon Web Services (Amazon Web Services) resources and the applications you run on Amazon Web Services in real time. You can use CloudWatch to collect and track metrics, which are the variables you want to measure for your resources and applications. CloudWatch alarms send notifications or automatically change the resources you are monitoring based on rules that you define. For example, you can monitor the CPU usage and disk reads and writes of your Amazon EC2 instances. Then, use this data to determine whether you should launch additional instances to handle increased load. You can also use this data to stop under-used instances to save money. In addition to monitoring the built-in metrics that come with Amazon Web Services, you can monitor your own custom metrics. With CloudWatch, you gain system-wide visibility into resource utilization, application performance, and operational health.

Amazon Redshift Overview This is an interface reference for Amazon Redshift. It contains documentation for one of the programming or command line interfaces you can use to manage Amazon Redshift clusters. Note that Amazon Redshift is asynchronous, which means that some interfaces may require techniques, such as polling or asynchronous callback handlers, to determine when a command has been applied. In this reference, the parameter descriptions indicate whether a change is applied immediately, on the next instance reboot, or during the next maintenance window. For a summary of the Amazon Redshift cluster management interfaces, go to Using the Amazon Redshift Management Interfaces. Amazon Redshift manages all the work of setting up, operating, and scaling a data warehouse: provisioning capacity, monitoring and backing up the cluster, and applying patches and upgrades to the Amazon Redshift engine. You can focus on using your data to acquire new insights for your business and customers. If you are a first-time user of Amazon Redshift, we recommend that you begin by reading the Amazon Redshift Getting Started Guide. If you are a database developer, the Amazon Redshift Database Developer Guide explains how to design, build, query, and maintain the databases that make up your data warehouse.

Amazon FSx is a fully managed service that makes it easy for storage and application administrators to launch and use shared file storage.

Amazon Augmented AI (Amazon A2I) adds the benefit of human judgment to any machine learning application. When an AI application can't evaluate data with a high degree of confidence, human reviewers can take over. This human review is called a human review workflow. To create and start a human review workflow, you need three resources: a worker task template, a flow definition, and a human loop. For information about these resources and prerequisites for using Amazon A2I, see Get Started with Amazon Augmented AI in the Amazon SageMaker Developer Guide. This API reference includes information about API actions and data types that you can use to interact with Amazon A2I programmatically. Use this guide to: Start a human loop with the StartHumanLoop operation when using Amazon A2I with a custom task type. To learn more about the difference between custom and built-in task types, see Use Task Types. To learn how to start a human loop using this API, see Create and Start a Human Loop for a Custom Task Type in the Amazon SageMaker Developer Guide. Manage your human loops. You can list all human loops that you have created, describe individual human loops, and stop and delete human loops. To learn more, see Monitor and Manage Your Human Loop in the Amazon SageMaker Developer Guide. Amazon A2I integrates APIs from various AWS services to create and start human review workflows for those services. To learn how Amazon A2I uses these APIs, see Use APIs in Amazon A2I in the Amazon SageMaker Developer Guide.

Describes all of the AWS IoT 1-Click device-related API operations for the service.
Also provides sample requests, responses, and errors for the supported web services
protocols.

Provides AWS Marketplace business intelligence data on-demand.

Welcome to the AWS Device Farm API documentation, which contains APIs for: Testing on desktop browsers Device Farm makes it possible for you to test your web applications on desktop browsers using Selenium. The APIs for desktop browser testing contain TestGrid in their names. For more information, see Testing Web Applications on Selenium with Device Farm. Testing on real mobile devices Device Farm makes it possible for you to test apps on physical phones, tablets, and other devices in the cloud. For more information, see the Device Farm Developer Guide.

Amazon DevOps Guru is a fully managed service that helps you identify anomalous behavior in business critical operational applications. You specify the AWS resources that you want DevOps Guru to cover, then the Amazon CloudWatch metrics and AWS CloudTrail events related to those resources are analyzed. When anomalous behavior is detected, DevOps Guru creates an insight that includes recommendations, related events, and related metrics that can help you improve your operational applications. For more information, see What is Amazon DevOps Guru. You can specify 1 or 2 Amazon Simple Notification Service topics so you are notified every time a new insight is created. You can also enable DevOps Guru to generate an OpsItem in AWS Systems Manager for each insight to help you manage and track your work addressing insights. To learn about the DevOps Guru workflow, see How DevOps Guru works. To learn about DevOps Guru concepts, see Concepts in DevOps Guru.

Amazon Kinesis Data Analytics is a fully managed service that you can use to process and analyze streaming data using Java, SQL, or Scala. The service enables you to quickly author and run Java, SQL, or Scala code against streaming sources to perform time series analytics, feed real-time dashboards, and create real-time metrics.

Contact Lens for Amazon Connect enables you to analyze conversations between customer and agents, by using speech transcription, natural language processing, and intelligent search capabilities. It performs sentiment analysis, detects issues, and enables you to automatically categorize contacts. Contact Lens for Amazon Connect provides both real-time and post-call analytics of customer-agent conversations. For more information, see Analyze conversations using Contact Lens in the Amazon Connect Administrator Guide.