Anthony Chu

Azure Static Web Apps is a great place to host Blazor WebAssembly apps. Among Static Web Apps' many features, it has built-in support for authentication using social logins. In this article, we'll look at how we can take advantage of Static Web Apps Authentication in our Blazor WebAssembly apps.

Overview

Azure Static Web Apps takes care of dealing with identity providers like GitHub, Twitter, and Facebook. The frontend application can then access the user's credentials using a built-in API.

Blazor WebAssembly supports custom authentication providers. We'll look at how to set up an auth provider for Static Web Apps and use it in the app. We'll also look at how to properly secure certain functionality in the app so that only authenticated users have access.

Azure Static Web Apps will soon have native support for building and deploying Blazor WebAssembly. For now, take a look at Tim Heuer's article on how to deploy it.

Azure Functions and Azure App Service recently added integration with OpenID Connect (OIDC) providers. We can now use any OpenId Connect compliant provider to authenticate users in our apps.

In this article, we'll look at how to configure Auth0 with Azure Functions. The same steps can be used to configure any other OIDC provider and can also be applied to Azure App Service.

Overview

To integrate an OpenID Connect provider with Azure Functions, we need to follow these steps:

1. Obtain a client id and secret plus other config settings from the OIDC provider.
2. Create an authentication config file in our app and add the relevant information from the OIDC provider to the file.
3. Supply the client secret in an app setting.
4. Enable file-based authentication configuration in the app.

Python runs on IoT devices. It runs on Azure Functions. It runs pretty much anywhere!

In this article, we'll look at how to build an end-to-end IoT solution using Python. We'll send telemetry from a device to Azure IoT Hub. Then we'll use Azure Functions and Azure SignalR Service to send messages from IoT Hub to a Python app in real-time.

Azure SignalR is a fully managed service that makes it easy to add highly-scalable real-time messaging to any application using WebSockets and other protocols. SignalR Service has integrations for ASP.NET and Azure Functions. Other backend apps can use the service's RESTful HTTP API.

In this article, we'll look at the benefits of using Azure SignalR Service for real-time communication and how to integrate it with a Java Spring Boot chat application using the service's HTTP API.

This article is part of #ServerlessSeptember. You'll find other helpful articles, detailed tutorials, and videos in this all-things-Serverless content collection. New articles are published every day — that's right, every day — from community members and cloud advocates in the month of September.

Find out more about how Microsoft Azure enables your Serverless functions at https://docs.microsoft.com/azure/azure-functions/.

Azure Functions recently announced the general availability of their Python language support. We can use Python 3.6 and Python's large ecosystem of packages, such as TensorFlow, to build serverless functions. Today, we'll look at how we can use TensorFlow with Python Azure Functions to perform large-scale machine learning inference.

Overview

A common machine learning task is the classification of images. Image classification is a compute-intensive task that can be slow to execute. And if we need to perform classification on a stream of images, such as those coming from an IoT camera, we would need to provision a lot of infrastructure to get enough computational power to keep up with the volume and velocity of images that are being generated.

Because serverless platforms like Azure Functions automatically scale out with demand, we can use them to perform machine learning inferencing and be confident that they can keep up with high-volume workloads.

Without scaling, this is how slowly our app currently runs: