Tag Archives: Microsoft Azure

Continuous delivery for Xamarin.iOS projects with TeamCity, FAKE and HockeyApp

via Customize Your Xamarin.Forms App With Pages For Each Platform | Xamarin Blog.

When I was building Moments, a Snapchat clone built with Xamarin.Forms and Microsoft Azure, I needed a way to show a live, in-app camera feed so users could take all the selfies their hearts desire without having to leave the app to take a photo. This type of camera access is possible in traditional Xamarin.iOS and Xamarin.Android development, which I learned how to do from ourXamarin recipes, but I knew that this type of camera access wasn’t part of Xamarin.Forms’ out-of-the-box 40+ controls, layouts, and pages.

Luckily, not only can you extend existing controls and build your own controlsin Xamarin.Forms, you can also render platform-specific pages from within your Xamarin.Forms apps. This was the type of customization that I needed, and it was surprisingly simple to implement.

Hosting NodeJS on Microsoft Azure

via Hosting Node.js on Microsoft Azure.

Got a fresh Node.js application to deploy? Awesome! Microsoft Azure has several options for hosting Node.js applications. In this article, we will be looking specifically at deploying to Windows Server websites and virtual machines.

Azure Insider: Real-World Scenarios for NodeJS in Microsoft Azure

via Azure Insider – Real-World Scenarios for Node.js in Microsoft Azure.

The popular quote, “If all you have is a hammer, everything looks like a nail,” certainly applies to software architecture. The best developers, however, understand a wide variety of frameworks, programming languages and platforms so they can engineer solutions that not only fulfill immediate business requirements, but also result in solutions that are scalable, maintainable, extensible and reusable. Node.js burst onto the scene three years ago, offering yet another tool for creating server-side software systems that support scalable Internet applications. Like all development tools, Node.js is not a magic hammer, and its capabilities should be fully understood before deciding if it’s the right fit for the solution at hand.

Introducing Premium Storage: High-Performance Storage for Azure Virtual Machine Workloads

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We are excited to announce the preview of the Microsoft Azure Premium Storage Disks. With the introduction of new Premium Storage, Microsoft Azure now offers two types of durable storage: Premium Storage and Standard Storage. Premium Storage stores data on the latest technology Solid State Drives (SSDs) whereas Standard Storage stores data on Hard Disk Drives (HDDs).

Premium Storage is specifically designed for Azure Virtual Machine workloads requiring consistent high performance and low latency. This makes them highly suitable for I/O-sensitive SQL Server workloads. Premium Storage is currently available only for storing data on disks used by Azure Virtual Machines.

DocumentDB (Day 5 of 31)

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DocumentDB is a NoSQL Document database offered as a service. It is full managed by Microsoft Azure and is extremely scalable and fast.
It gives us the possibility to store any kind of data. The stored data don’t need to have a specific format or to respect a model predefined. We can store in the same collection, data with different models and formats.

Getting Started on Azure Machine Learning – Part 1

Earlier this summer at WPC, we announced the preview of Microsoft Azure Machine Learning, a fully-managed cloud service for building predictive analytics solutions. With this service, you can overcome the challenges most businesses have in deploying and using machine learning. How? By delivering a comprehensive machine learning service that has all the benefits of the cloud. In mere hours, with Azure ML, customers and partners can build data-driven applications to predict, forecast and change future outcomes – a process that previously took weeks and months. Read more>>

Introducing Microsoft Azure Machine Learning

Microsoft Azure Machine Learning (MAML) is a service on Windows Azure which a developer can use to build a predictive analytics model using machine learning over data and then deploy that model as a cloud service. ML Studio provides functionality to support the end-to-end workflow for constructing a predictive model, from ready access to common data sources, data exploration, feature selection and creation, building training and testing sets, machine learning over data, and final model evaluation and experimentation. In this presentation, we present an overview of the basic data science workflow, with details on select machine learning algorithms, then take you on a guided tour of ML Studio. During the presentation we will build a predictive analytics model using real-world data, evaluate several different machine learning algorithms and modeling strategies, then deploy the finished model as a machine learning web service on Azure within minutes. This end-to-end description and demonstration is intended to provide sufficient information for you to begin exploring ML Studio on your own after the session.