Anthony Chu joins Donovan Brown to show how to deliver live updates from Azure Functions to web, mobile, and desktop apps with Azure SignalR Service.

Learn how to send real-time messages over WebSockets from your serverless apps with a few lines of code.

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In this video, take a deeper look at alternative ledger technologies. We look at developer experiences for Corda using the R3 extension for VS Code.

Additional details and sample code are available on GitHub: 

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In this video, learn why and how to track assets and code you’re creating in an end to end machine learning workflow.

Time Index:

  • [01:20] How to track assets and artifacts
  • [04:20] Demo – How to keep track of code
  • [05:58] Why it’s important to manage datasets + Demo

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In this video, learn how you can use Azure Event Grid and Azure Machine Learning to trigger and consume machine learnings events. We talk about why eventing is important and how you can enable scenarios such as run failure alerts and retraining models.

Jump To:

  • [00:50] What is Event Grid?
  • [01:32] Why is this useful?
  • [02:32] Demo – How to set up an event subscription
  • [03:40] Demo – How to filter events
  • [05:30] Demo – Logic app example


In this episode, Damien sits down with Alison Hawke, QA practice lead, World Wide Technology, to talk about how changing the way your team does testing can improve your software, and make your lives more fun.

With bonus eavesdropping, apprentices, donuts, and soak tests. They talk about what to automate, how to train a newbie QA, and why QA people have one of the best jobs in software.

Time Index:

[03:06] QA people eavesdrop all the time

[03:11] Bribery as a means to better software

[04:38] Test automation, what and why

[06:45] Soak testing to make small defects huge

[07:21] Starting a QA practice in a company

[08:35] Training QA apprentices

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Think serverless is just for functions? Think again!

Brendan Burns joins Donovan Brown to look at how serverless containers can provide a cloud-native container experience without the worry of a server or operating system.

They also look at how this integrates with the Azure Kubernetes Service (AKS).

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Here’s an interesting write up on how to integrate machine learning models into Azure Stream Analytics.

You can implement machine learning models as a user-defined function (UDF) in your Azure Stream Analytics jobs to do real-time scoring and predictions on your streaming input data. Azure Machine Learning allows you to use any popular open-source tool, such as Tensorflow, scikit-learn, or PyTorch, to prep, train, and deploy models.

Thomas Maurer joins Donovan Brown to show how you can manage and govern your Windows and Linux machines hosted outside of Azure on your corporate network or other cloud provider, similarly to how you manage native Azure virtual machines.

When a hybrid machine is connected to Azure, it becomes a connected machine and is treated as a resource in Azure


In part 2 of a 3 part series, focused on the Bot Framework, this episode looks at how you can use the telemetry capture capabilities built into the Bot Framework to analyze your bots usage and gain actionable insights by exploring data such as user / conversation trends, channel breakdown and dialog completion vs abandonment.

We discuss why bot analytics are crucial, take a look at how easy it is to enable telemetry capture within your bot and how to drill into your data using Azure and Power BI.


  • [00:40] – Discussion about why bot analytics are important
  • [02:15] – Demo showing how telemetry is wired up within a .NET Core sample bot
  • [04:40] – Viewing telemetry captured during a debug session within Visual Studio
  • [06:40] – Analyzing your telemetry within Application Insights in Azure and creating a dashboard
  • [10:20] – Open source Power BI dashboard for advanced bot analytics

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