Channel 9

Ayşegül Yönet, @AysSomething, covers the differences between AR, VR, and MR/XR. More info at: http://msft.social/cblTCl

In this episode, Jeremy chats with software engineer Matías Quaranta about some of the lesser known features of the Cosmos DB SDK for .NET. Matias discusses some useful patterns for managing the lifetime of the client, implementing custom serializers and some other interesting features. [01:09] - Using the fluent pattern with the Cosmos client[05:28] - Custom serialization[11:08] - Working with Dependency injection and Stream Useful Links GitHub repo with the sample codePerformance tips for Azure Cosmos DB and .NETWork with NoSQL data in Azure Cosmos DB (MS Learn)Cosmos DB .NET Standard SDK release notes

In this episode, Abel sits down again with April Edwards to talk about using Terraform to provision infrastructure through pipelines with the help of Azure DevOps By using Azure DevOps and Terraform, we're able to simplify processes from build to deployment. Gone are the days of having to do everything manually- now with a user-friendly UI, we can automate and version control with ease. April walks through the basics of pipelines, storage accounts, replacing tokens, why we deploy to the latest version of Terraform, and the benefits of automatic versioning built into Terraform. Companies want to enable logging, track, enable borders, and it's all here built in and integrated with Azure DevOps! [00:20] - Terraform with pipelines intro[01:20] - Pipelines intro[03:30] - Release pipelines demo [04:40] - State Files demo [06:40] - Versioning demo [07:55] - Authentication demoFollow April on Twitter: https://twitter.com/TheAprilEdwardsFollow Abel on Twitter: https://twitter.com/AbelSquidHead

For the next few weeks of AI Show, we are taking a bit of a different tack (let us know if you want more or less of this kind of content). I was invited to present at the Toronto AI User Group in early January and decided to bring along some gear to record the talk. The talk was about an hour long so I split it up into 4 distinct parts to make it more digestible. In this part of the talk I go into three distinct concepts and how they work internally. [01:35] What is the difference between AI, Machine Learning, and Deep Learning? [04:55] When is Machine Learning an appropriate tool to solve problems? [08:28] What is a machine learning model? Hope you enjoy part 1! As always feel free to send any feedback or add any comments below if you have any questions. The AI Show's Favorite links: Don't miss new episodes, subscribe to the AI Show Create a Free account (Azure) Deep Learning vs. Machine Learning Get Started with Machine Learning

[00:37] - Microsoft Ignite the Tour [01:01] - Visual Studio Code 1.42 [01:36] - Visual Studio for Mac 8.5 Preview 2 and Details for Xamarin Developers[02:05] - Visual Studio for Mac Refresh(); Online Event[02:22] - .NET Interactive, .NET Interactive GitHub, and PowerShell Support for Jupyter Notebooks Details[03:29] - GitHub Desktop 2.3[04:22] - This Week on Channel 9: IoT Show, The AI Show, and DevOps Lab[04:51] - Christina's Pick of the Week: Lumiere Brothers in 4K and YouTube link Please leave a comment or email us at twc9@microsoft.com. Follow @CH9 Follow @CH9 Create a Free Account (Azure)

The Vision AI DevKit (https://aka.ms/iotshow/visionaidevkit) is a smart camera device powered by Azure IoT Edge.Equiped with a camera and microphones, it allows developing proof of concept (or even actual) projects implementing advanced image and sound analytics directly on the device. In a previous episode (https://aka.ms/iotshow/185), Mahesh Yadav showed us the unboxing and first time experience with the device. In this new episode, Mahesh is back to show us how you can train and deploy new AI models on the device in a matter of minutes. Check out the Vision AI devkit site to learn more and take it for a spin: https://aka.ms/iotshow/visionaidevkit

Curious to see the Open Application Model (OAM) in action? Look no further than Rudr - the Kubernetes reference implementation of OAM. In this episode, Mackenzie Olson and Sudhanva Huruli join Donovan Brown to demo how Rudr provides clear separation of concerns for DevOps practices for application developers, application operators, and infrastructure operators. With Rudr, we describe our distributed application, and then run two instances on AKS and GKE respectively. [0:01:25] - Demo Rudr - A Kubernetes Implementation of the Open Application ModelService Tracker app (OAM samples repo)Open Application Model siteThe Open Application Model specificationOpen Application Model Gitter (oam-dev)Azure Kubernetes Service (AKS)Learn all about Open Application Model (OAM)Create a free account (Azure)

We'll talk about what is Spark and using it in Big Data Clusters.More info: https://docs.microsoft.com/en-us/sql/big-data-cluster/tutorial-data-pool-ingest-spark?view=sql-server-ver15 [00:00] Introduction[00:30] One-sentence definition of Spark[00:47] Storing Big Data[01:44] What is Spark?[02:35] Language choice[03:27] Unified compute engine[04:57] Spark with SQL Server[05:47] Learning more[06:10] Wrap-up

In this episode, Abel sits down again with April Edwards to talk about Terraform Modules and how to deploy reusable and repeatable code. Testing code in multiple different environments can be difficult, particularly if those environments require several different variables or distinct geographic regions. April walks through the creation of modules, their use in streamlining developer teams for local and global organizations, how a Terraform Workspace works, and how to use them to manage your environments and create more efficient operations teams! [00:20] - What are modules?[01:30] - Modules demo[07:30] - Terraform Workspace demo[08:30] - Driving efficiencies with workspaces and modules Follow April on Twitter: https://twitter.com/TheAprilEdwardsFollow Abel on Twitter: https://twitter.com/AbelSquidHead

Visual Studio Code offers many great features for Data Scientists and Python developers alike, allowing you to explore and experiment on your data using the flexibility of Jupyter Notebooks combined with the power and productivity of VS Code. Tune in to learn how to supercharge your Jupyter Notebooks with VS Code. Jump To: [00:56] – Creating a new notebook in Visual Studio Code[01:28] – Notebook Editor interface[03:53] – Bringing existing Notebooks to Visual Studio Code[06:22] – Converting your Jupyter Notebook into Python code[08:15] – Connecting to remote Jupyter Server Learn More: Visual Studio Code DownloadVisual Studio Code Python extensionJupyter Notebooks in VS Code documentationThe AI Show's Favorite links: Don't miss new episodes, subscribe to the AI Show Create a Free account (Azure) Deep Learning vs. Machine Learning Get Started with Machine Learning

Learn how to use audio from the Vision AI DevKit as input data for IoT solutions. (aka.ms/iotshow/audiooniotedge) Using AI on the edge, you can monitor the sound of a bearing on a motor, the sound of RPMs of a vehicle, or the sound of suction of a vacuum pump and detect failures when a sound changes. In this episode, Kevin Saye, Senior Technical Specialist, outlines five steps for processing audio with the Vision AI Developer Kit. First, you'll collect audio samples of a device (in this case, a water fountain) when it's working. Secondly, you'll label them. Thirdly, you'll build a neural network for processing the audio files. Next, you'll build the Azure IoT Edge models for inferencing the audio. Finally, you'll deploy the modules to Azure IoT Edge. Check out this sample solution for processing audio with a Vision AI DevKit: aka.ms/iotshow/audiooniotedge

In this session we show how to use the Blockchain Developer extension for VS Code together with blockchain data manager to simplify development. For more information head over to: https://aka.ms/bcvscode Related Links: Azure Blockchain SolutionsAzure Blockchain ServiceAzure Blockchain WorkbenchAzure Blockchain TokensAzure Free TrialAzure Blockchain Dev KitAzure Blockchain DocumentationFollow @CH9 Follow @MSFTBlockchain

Want to be more involved with changes in Terminal? Kayla Cinnamon, @cinnamon_msft, tells us how to get started.

With Farmbeats, Microsoft is making data-driven agriculture simple and affordable. Watch this intriguing episode to learn about Farmbeats, (aka.ms/iotshow/farmbeats) a new Azure offering currently in preview and available on Azure Marketplace. Understand how Farmbeats enables partners to make farmers more efficient by providing visibility into how much water is in the soil, what the soil conditions are, how plants are growing, and more. Learn from Dr. Ranveer Chandra, Chief Scientist, Azure Global, about the amazing solution to the challenge of connecting to farms (leveraging unused TV channels!) developed by Microsoft. Hear how data is stored in Farmbeats Datahub and analyzed using AI and ML to provide valuable insights like where sensors should be optimally placed. Jeff Hollan, Principal PM Manager, shows how simple it is to use IoT Plug and Play to connect a new sensors, drones, or robots to Farmbeats. Jeff demonstrates how easy it is for a partner to enable a farmer to view pressure, temperature, and humidity from a sensor on his/her farm using Azure IoT Central. Learn more aka.ms/iotshow/farmbeats Try Farmbeats aka.ms/iotshow/farmbeatsonmarketplace

In this video we are going through the high availability options you have for the mission critical services running within the SQL Server Big Data Clusters. Find out more here: High AvailabilityHigh Availability for HDFS and Spark[00:00] - Introduction[01:00] - Storage recap for Big Data Clusters[02:47] - High Availability options[04:34] - High Availability for SQL Server master instance[06:18] - Availability Groups in Big Data Clusters[07:07] - High Availability for HDFS[07:51] - High Availability for Spark[08:27] - Demo: configuring High Availability in Big Data Clusters[10:55] - Getting started[11:25] - Summary and wrap-up