学习通过代码与 Azure 服务交互
Learn how to use the Cognitive Services SDKs with these samples
Learn how to use Azure Container Instances with Logic Apps to deploy containers in an event-driven way.
Calling a ASP.NET Core Web API from a WPF application using Azure AD v2.0
This project contains advanced VOD media workflow examples of using Azure Functions v2 with Azure Media Services v3. The project includes several folders of sample Azure Functions for use with Azure Media Services that show workflows related to ingesting content directly from blob storage, encoding, and writing content back to blob storage.
Sample Azure Functions for use with Azure Media Services. Ingest from Azure Blobs, encode and output to Azure Blobs, monitor encoding progress, and use WebHooks or Queues to hook into the workflow.
An ASP.NET Core 2.x Web App which lets sign-in users (including in your org, many orgs, orgs + personal accounts, sovereign clouds) and call Web APIs (including Microsoft Graph)
An ASP.NET Core web application that authenticates Azure AD users and calls a web API using OAuth 2.0 access tokens.
This repository contains a set of easy-to-understand, continuously-tested C# samples that can be deployed on an Azure Data Box Edge device.
Azure Blob Storage Photo Gallery Web Application using ASP.NET MVC 5. The sample uses the .NET 4.5 asynchronous programming model to demonstrate how to call the Storage Service using the Storage .NET client library's asynchronous APIs.
Quick sample of how to connect IoT Hub with Azure Functions for messages processing
A sample Python solution showing how to authenticate against Azure Active Directory (AAD) before using the Azure Data Lake Analytics (ADLA) Python SDKs.
A generic azure function that can be used to convert any console application to an HTTP webservice
A web application (written in .NET 4.5) that shows how to perform single sign out from all Azure AD apps using OpenID Connect distributed sign out.
This reference architecture walks you through the decision-making process involved in designing, developing, and delivering a serverless application using a microservices architecture through hands-on instructions for configuring and deploying all of the architecture's components along the way. The goal is to provide practical hands-on experience in working with several Azure services and the technologies that effectively use them in a cohesive and unified way to build a serverless-based microservices architecture.
Sample showing how to deploy a AI model from the Custom Vision service to a Raspberry Pi 3 device using Azure IoT Edge