Channel 9

An Ugly Sweater is great—but what's even better (aka.ms/IoTShow/UglySweater) is an IoT-enabled Ugly Sweater. In this episode of the IoT Show, Olivier Bloch is joined by Jim Bennett, a Senior Cloud Advocate at Microsoft. Jim has built an Ugly Sweater using Azure IoT Central, Microsoft's IoT app platform, and a Raspberry Pi Zero. Dive into Jim's Python code and learn how Azure IoT Central is able to connect IoT devices to the cloud faster than any other platform. See how Jim uses IoT Central's extensibility APIs to control his Ugly Sweater via Twitter. This code is on GitHub for you to play with and extend. Get started today! Project Code on GitHubAzure IoT Central OverviewAzure IoT Central Learning PathAzure IoT Central Documentation

Tim Sander joins Scott Hanselman to discuss composite indexes and correlated subqueries using the SQL API in Azure Cosmos DB. A Request Unit, or RU, is the measure of throughput in Azure Cosmos DB. Learn how to optimize queries with a composite index to decrease the RUs needed for a given query. They also showcases correlated subqueries and examples of how they can make it easier to query arrays in Azure Cosmos DB. [0:01:15] - Demo: composite indexes [0:04:20] - Demo: correlated subqueries Indexing policies in Azure Cosmos DB - Composite IndexesSQL subquery examples for Azure Cosmos DBAzure Cosmos DB workshop labs (GitHub)Azure Cosmos DB overviewCreate a free account (Azure)

Anavi Nahar joins Donovan Brown to show how virtual network peering enables you to connect networks seamlessly in Azure Virtual Network. The virtual networks appear as one for connectivity purposes. The traffic between virtual machines uses the Microsoft backbone infrastructure. Like traffic between virtual machines in the same network, traffic is routed through Microsoft's private network only. [0:02:08] - Demo Azure Virtual Network peeringWhat is Azure Virtual Network?Azure Virtual Network overviewTroubleshoot virtual network peering issuesCreate a free account (Azure)

In this session, we'll be covering an overview of compute pools in Big Data Clusters. Learn more about Compute Pools.

Show how organizations do more with their data using the and AI capabilities of Azure Cognitive Search. I'll show how they can take data from databases and files and easily mine it for additional knowledge, allowing them to better explore and understand their information. Additional information: DocsThe AI Show's Favorite links: Don't miss new episodes, subscribe to the AI Show Create a Free account (Azure) AI Blog Fast ML MIT News | AI Medium | Francesca Lazzeri Deep Learning vs. Machine Learning Get Started with Machine Learning

Microsoft Learn, at http://aka.ms/Learn, is the easiest way to learn products and services through task-based, interactive learning. With hundreds of free courses, localized into 23 different languages, covering Azure, Dynamics, Power Apps, Flow, with more coming in the future! Whether you're just starting or an experienced professional, our hands-on approach helps you arrive at your goals faster, with more confidence and at your own pace.

In this episode, Robert is joined by Jeffrey Mew, who shows how you can natively edit Jupyter notebooks in Visual Studio Code. Jupyter (formerly IPython) is an open-source project that enables you to easily combine Markdown text and executable Python source code on one canvas called a notebook. These notebooks contain live code, equations, visualizations and narrative text. Jeffrey shows how easy it is to work with Jupyter notebooks in Visual Studio Code. Resources: Getting started with Python and VS Code: https://aka.ms/vst/pythonvscodeAnnouncing Support for Native Editing of Jupyter Notebooks in VS CodeWorking with Jupyter Notebooks in Visual Studio CodePython extension for Visual Studio Code

In this episode, Jeff Hollan from the Azure Functions team comes to discuss the benefits of pairing Blazor webassembly applications along with Azure Functions. [01:12] - What are the benefits of Blazor in Serverless?[04:02] - Why does Blazor fit well with Serverless ?[09:56] - How to get started with Blazor and Azure Functions?[14:15] - Community call out[15:47] - How to reach out to the Azure Functions team? Useful Links Learn about BlazorBlazor and Azure Functions for Serverless websitesGet started with Azure FunctionsJeff's Blog

A critical characteristic of cloud native architectures it the ability to dynamically scale and support massive numbers of users, events, and requests on these distributed applications and teams. Serverless can help in handling these challenges without having to worry about infrastructure or even an entire application. Jeff jumps into the show production and chats with us on how this fits into the plan for cloud native apps. [02:20] – What are the benefits?[04:50] – Are there other challenges?[07:43] – Jeff shows an awesome demo on scaling![11:45] – How do we think about serverless as a part of our solution first?[14:20] – Running serverless in a container[16:30] – Where is serverless going? [20:30] – Tooling for Azure Functions and Logic Apps Links: Free Azure Account - https://aka.ms/cldntvshw/azure Azure Functions - https://aka.ms/cldntvshw/functions Azure Logic Apps - https://aka.ms/cldntvshw/logicapps Azure Application Insights - https://aka.ms/cldntvshw/appinsights

This video talks about the new ML assisted data labeling capability in Azure Machine Learning Studio. You can create a data labeling project and either label the data yourself, or take help of other domain experts to create labels for you. Multiple labelers can use browser based labeling tools and work in parallel. As human labelers create labels, an ML model is trained in the background and its output is used to accelerate the data labeling workflow in various ways such as active learning, task clustering, and pre-labeling. Finally, you can export the labels in different formats. [00:32] – Introduction to data labeling[01:55] – Creating a data labeling project in Azure ML Studio[11:30] – Labeling the data yourself[13:40] – Adding other labelers to your data labeling project[15:15] – Labeling from the labeler portal[16:30] – What is coming in future Learn More: Learn how to create data labeling projectHow to label imagesThe AI Show's Favorite links: Don't miss new episodes, subscribe to the AI Show Create a Free account (Azure) AI Blog Fast ML MIT News | AI Medium | Francesca Lazzeri Deep Learning vs. Machine Learning Get Started with Machine Learning

In this episode, Robert is joined by Tyreke White, who shows us how to get started with Python development using Visual Studio Code. He covers what you need to install and how to configure your environment, as well as sharing tips on how to debug your Python code. Resources Getting started: https://aka.ms/vst/pythonvscodeFollow our blog: https://aka.ms/pythonblogFollow us on Twitter: @pythonvscodeDemo code

Let's take a step back in a new mini-series that I like to call Xamarin.Forms 101. In each episode we will walk through a basic building block of Xamarin.Forms to help you build awesome cross-platform iOS, Android, and Windows applications in .NET. This week we take a look at how to effectively use the Entry control and the Editor control in Xamarin.Forms. Show Notes: Xamarin.Forms Entry DocumentationXamarin.Forms Editor DocumentationNever Miss an Episode: Follow @TheXamarinShowUseful Links: Learn more about Xamarin, Xamarin.Forms, & Cross-platform developmentXamarin Developer Center & BlogMicrosoft Learn Self-Guided TrainingCreate a Free Account (Azure)Xamarin Developers YouTube ChannelXamarin on Twitter

Azure Machine Learning compute instances (formerly Notebook VMs) is a hosted PaaS offering that supports the full lifecycle of inner-loop ML development--from model authoring, to model training and model deployment. AzureML Compute Instances are deeply integrated with AzureML workspaces and provide a first-class experience for model authoring through integrated notebooks using AzureML Python and R SDK. [03:35] – Demo start [06:50] – Creating Compute Instance [10:44] – Using native Jupyter [13:20] – Collaborative debugging using Compute Instance sharing Learn More: What is Azure Machine LearningThe AI Show's Favorite links: Don't miss new episodes, subscribe to the AI Show Create a Free account (Azure) AI Blog Fast ML MIT News | AI Medium | Francesca Lazzeri Deep Learning vs. Machine Learning Get Started with Machine Learning

Sam George, CVP for Azure IoT, gives an overview of Azure IoT announcements made at the Microsoft Ignite held November 4-8, 2019, in Orlando, FL. He explains how Microsoft is simplifying IoT, delivering IoT security end-to-end, and making the Edge more intelligent. For more details, check out Sam's Ignite session

Learn about the new features Automated Machine Learning (Automated ML) is releasing, from enhanced time-series forecasting capabilities and no-code user interface, to DNN support for text and forecasting scenarios. [00:30] What is Automated ML? [00:50] What is time series forecasting? [01:30] Forecasting capabilities. [02:50] Requirements [04:20] Demo Learn More: Learn MoreThe new studioNotebook SamplesThe AI Show's Favorite links: Don't miss new episodes, subscribe to the AI Show Create a Free account (Azure) AI Blog Fast ML MIT News | AI Medium | Francesca Lazzeri Deep Learning vs. Machine Learning Get Started with Machine Learning