Malte Lantin

Moving somewhere else...

Malte Lantin — Mon, 04 Jun 2018 08:57:09 GMT

I am currently looking for a place to go next. Don't expect new content on this page. 👋

Handling LUIS.ai (Language Understanding Intelligent Service) Keys

Malte Lantin — Thu, 21 Sep 2017 21:49:06 GMT

When working with our Language Understanding Intelligent Service (https://www.luis.ai) for natural language understanding you will need your own subscription keys sooner or later. These subscription keys are issued through the Microsoft Azure Management Portal (https://portal.azure.com) and the billing is handled through your Azure Subscription. When adding your keys in the Keys section of the LUIS portal you might stumble over some problems. On of these problems might be a strange "Bad Argument, Invalid Subscription Key" message. To avoid this, there are a few things to know:

At his moment (Sept 2017) there are different deployments of LUIS for different regions of the world.
- https://www.luis.ai for the North America Deployment
- https://eu.luis.ai for the Europe Deployment
When you create a Key for LUIS on the Azure Portal you will be asked for a Region. This region should match the region of the LUIS deployment you are using.

This means when you want to use LUIS from our Europe Region you should create you LUIS key for the European Region as well. You will find the Keys in the "Keys" section of you LUIS subscription on the Azure Portal. Now you can enter your matching key on the Keys section of the LUIS website (https://eu.luis.ai/keys or https://www.luis.ai/keys) I hope this helps to save some time getting LUIS set up.

Using GPU-powered Virtual Machines in the Cloud for your Machine Learning and Deep Learning workloads

Malte Lantin — Thu, 03 Aug 2017 16:22:24 GMT

Until a few years ago running Machine Learning (ML) and Deep Neural Networks required owning really powerful computers with lots of CPUs or even clusters of machines. With the adoption of GPUs for non-graphics related workloads new opportunities became available. You can now run many of your workloads in a much shorter period of time making use of modern GPUs you already have instead of using CPUs. The good thing is, you do not even have to own your own servers equipped with pricy graphics hardware any more. As we have become used to for CPU-powered machines we can now also get GPU powered machines on-demand in the cloud. Microsoft Azure offers two categories of GPU powered machines with our N-Series. NC VMs feature NVIDIA Tesla K80 GPUs for compute and machine learning workloads. NV VMs are tailored towards 3d workloads and rendering and feature NVIDIA Tesla M60 GPUs. Learn more in these VM sizes in this announcement. So how do you get started for Machine Learning and Deep Learning workloads? The easiest way to get going (after getting your Azure account) is to start up a GPU powered VM, based on our Data Science VM template. This VM template has the NVIDIA CUDA Toolkit with driver, CUDA and cuDNN already installed. The template is available both with Linux and Windows OS. There are small differences between the software installed, but they both feature popular runtimes and almost all relevant tools for Machine Learning and Deep Learning on GPUs and CPUs. Check this website for a full list of preinstalled tools. The VM is really easy to set up in Azure and you can choose between lots of VM sizes in Azure, both with or without GPUs. The only thing to keep in mind is, that GPU VMs are not available in all of the Azure regions. Check this overview for the availability of NC-Series VMs.

I don't need all these tools

What if you do not need all the tools included in the Data Science VM and you want to get started with your own VM and tools? There are just a few steps to go and a few choices to make. I collected most relevant links for you and will describe most of the steps to get going with a GPU-powered Linux VM here:

Start you own NC-Series VM using the Azure CLI or the Azure Portal. I recommend using Ubuntu Linux as most of the documentations have detailed steps for Ubuntu and you will get ready-to-use packages for most tools.
Log into your VM using SSH (on Windows you can use PuTTY or Bash on Ubuntu on Windows)

! You might need to download some of the following tools with a desktop browser and copy them to your VM via SCP !

Install the NVIDIA GPU drivers for Linux inside your VM. You can find a detailed guide on the Azure Website. Check that you install the lasted version of the drivers as the Azure Website could be outdated. You can also find a manual for installing the driver on the NVIDIA website.
If required by your workload or application you might also want to install the cuDNN for running deep neural networks on your VM. This download requires signing up for the NVIDIA developer program (which is free). For me downloading the drivers involved jumping through some additional hoops. The problems and solutions are described on this website. Especially the wrong file extension was confusing, but easy to fix.

Now that the required drivers and tools for GPU workloads are installed you get started with your workloads based on frameworks like Tensorflow, CNTK or Caffe.

Let's use containers

A quick way to get started after installing the NVIDIA driver is using Docker containers that already have the required tools installed. Luckily there is an extension for Docker that allows us to make use of a NVIDIA GPU inside our Docker containers: nvidia-docker. If using Ubuntu you can quickly get going with GPU powered container-workloads:

First install Docker on your Virtual Machine. There is a detailed guide on the Docker website.
After installing Docker, install Nvidia-Docker next. For Ubuntu this is just a few commands. You can find the prerequisites on GitHub as well.
Now you can launch your first GPU enabled container: nvidia-docker run --rm nvidia/cuda nvidia-smi

Many deep learning tools are already available as a Docker image. One example is H2O who already offer a Docker container including their GPU-powered Deep Learning environment. You will find all information on H2O Deepwater on GitHub. What is Deepwater (from the linked website)?

Native implementation of Deep Learning models for GPU-optimized backends (MXNet, Caffe, TensorFlow, etc.)
Train user-defined or pre-defined deeplearning models for image/text/H2OFrame classification from Flow, R, Python, Java, Scala or REST API

You can pull and start your H2O Deepwater container via: nvidia-docker run -it --rm opsh2oai/h2o-deepwater Whether you are inside your container or on your GPU-equipped host VM, you can monitor processes and GPU utilization with the nvidia-smi tool. You can monitor it live using: watch -d -n 1 nvidia-smi

If hope this information was useful for you to get started with GPU-powered Deep Learning workloads in the cloud. An don't forget to shut down your virtual machines after finishing your jobs or using the auto-shutdown feature in Azure VMs.

Links:

N-Series Virtual Machines in Azure: https://azure.microsoft.com/en-us/pricing/details/virtual-machines/series/#n-series
N-Series General Availability: https://azure.microsoft.com/en-us/blog/azure-n-series-general-availability-on-december-1/
Azure Trial Account: https://azure.microsoft.com/de-de/free/
Data Science VM: https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-data-science-virtual-machine-overview
Azure Services per Region: https://azure.microsoft.com/en-us/regions/services/
Create an Ubuntu VM using the Azure CLI: https://docs.microsoft.com/de-de/azure/virtual-machines/linux/quick-create-cli
Create an Ubuntu VM using the Azure Portal: https://docs.microsoft.com/de-de/azure/virtual-machines/linux/quick-create-portal
PuTTY: https://www.putty.org/
Bash on Ubuntu on Windows: https://msdn.microsoft.com/en-us/commandline/wsl/about
Install the NVIDIA CUDA drivers (Azure Website): https://docs.microsoft.com/en-us/azure/virtual-machines/linux/n-series-driver-setup#install-cuda-drivers-for-nc-vms
Install the NVIDIA CUDA drivers (NVIDIA Website): https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#ubuntu-installation
Install cuDNN: https://developer.nvidia.com/rdp/cudnn-download
Solving problems installing cuDNN: https://askubuntu.com/questions/916711/how-can-i-install-cudnn-on-ubuntu-14-04
Nvidia-Docker: https://github.com/NVIDIA/nvidia-docker/
Nvidia-Docker Quickstart: https://github.com/NVIDIA/nvidia-docker/
Nvidia-Docker Prerequisites: https://github.com/NVIDIA/nvidia-docker/wiki/Installation
H2O Deepwater: https://github.com/h2oai/deepwater

Create you first ASP.NET Core App and host it in a Linux Docker container on Microsoft Azure (Part 3/3)

Malte Lantin — Fri, 13 Jan 2017 11:26:24 GMT

In the third part of this post we will push our image to Docker Hub and then host our container on Microsoft Azure. You can find the first part of this post here.

Part 3: Publish your container on Microsoft Azure

First we are publishing our container by pushing it to Docker Hub . Of course you an also use your private registry as well. To use Docker Hub we will have to create a Docker ID. You can do this through Docker Hub. Now we can start by renaming / tagging our image:

docker tag firstaspapp DockerID/nameforyourimage

After you have done this log in with your ID and password:

docker login

Now you can push your image to Docker Hub. If you prefer to use a private repository you will have to create it on Docker Hub first.

docker push yourDockerID/nameofyourimage

If you now visit your profile on Docker Hub you should be able to see your pushed image. For me it took some time to show up here. Once your image is available on Docker Hub, we can publish our container to Microsoft Azure. To use Microsoft Azure you will need an Azure Subscription. There is a free trial subscription available if you don't have one yet. Log into the Azure Portal with your Microsoft or company account. Once on the Azure Portal click on the "+" to add a new resource and search for Web App on Linux. Azure Web Apps on Linux is a docker-based hosting environment that supports different programming languages like NodeJS, PHP and ASP.NET Core out of the box and offers you the opportunity to bring your own Docker containers. This is what we will use. First select a name for your web app, pick a subscription, name or select a resource group and create or select an app service plan. When creating a new app service plan you can decide which Azure region to use for your application. After you have done all this click on "Configure container" and then on "Docker Hub". Now select "Public" and enter the name for your image. If you chose to host your image in a private registry or private Docker repository you will also have to add your credentials here. Now confirm your settings and click on "Create". Creating you Azure Web App and starting your container will usually just take a few seconds. Once the Web App is created you can click on the URL for your web app to see your ASP.NET Core application running in a Linux Docker container on Microsoft Azure. Depending on the App Service plan you picked you can now make use of different Azure features like scaling your application up as needed. Links:

Docker Hub: https://hub.docker.com/
Microsoft Azure: https://azure.microsoft.com
Part 1 of this blog post: https://blogs.msdn.microsoft.com/malte_lantin/2017/01/12/create-you-first-asp-net-core-app-and-host-it-in-a-linux-docker-container-on-microsoft-azure-part-13/
Docker: https://www.docker.com/
Microsoft Azure Trial: https://azure.microsoft.com/en-us/free/
Azure Management Portal: https://portal.azure.com
Azure Web Apps on Linux Documentation: https://docs.microsoft.com/en-us/azure/app-service-web/app-service-linux-how-to-create-a-web-app

Create you first ASP.NET Core App and host it in a Linux Docker container on Microsoft Azure (Part 2/3)

Malte Lantin — Fri, 13 Jan 2017 10:12:34 GMT

In the second part of this blog post we will build a docker image for our ASP.NET Core App and run a container locally. You can find the first part of this blog post here.

Part 2: Host your application in a Docker container

To use docker images and create and run Docker containers we will first have to install Docker on our machine. Docker is available for Mac, Windows and Linux on the official Docker website. After you have installed Docker, you can use the "docker" command from your command line / terminal. Check your installation by typing

docker version

Now create a new file called "Dockerfile" inside the publish folder of your web application. You can use an editor of your choice to edit the file. I will use Visual Studio Code (Mac, Linux, Windows) as it supports syntax highlighting and auto-completion for Dockerfiles. Add the following content to your Dockerfile:

FROM microsoft/aspnetcore
WORKDIR /app
COPY . .
EXPOSE 80
ENTRYPOINT ["dotnet", "yourappname.dll"]

The first line sets the Microsoft/aspnetcore image as the base image. It is provided and maintained by Microsoft on Docker Hub. The second line creates and sets a working directory. Then the content from our "publish" folder is copied to the working directory. The next line exposes port 80 for our webserver. The last line defines our entrypoint with the command and corresponding arguments. Save your Dockerfile and build your Docker image from the command line (-t sets a name and tag for your image):

docker build . -t nameforyourimage

You can display all your images with the following command:

docker images

Now you now can create and run your Docker container (you can also just create the container with "docker create"):

docker run -p 8000:80 nameforyourimage

The -p flag maps published port 80 of your container to your host. You should now be able to access you application through https://localhost:8000/. In the third part of this blog post we will publish our images to Docker Hub and host our container on Microsoft Azure. You can find Part 3 here. Links:

Part 1 of this blog post: https://blogs.msdn.microsoft.com/malte_lantin/2017/01/12/create-you-first-asp-net-core-app-and-host-it-in-a-linux-docker-container-on-microsoft-azure-part-13/
Download Docker for you system: https://www.docker.com/products/overview
Visual Studio Code: https://code.visualstudio.com/
Dockerfile Reference: https://docs.docker.com/engine/reference/builder/
ASP.NET Core Image on Docker Hub: https://hub.docker.com/r/microsoft/aspnetcore/
Docker Hub: https://hub.docker.com/
Microsoft Azure: https://azure.microsoft.com

Create you first ASP.NET Core App and host it in a Linux Docker container on Microsoft Azure (Part 1/3)

Malte Lantin — Thu, 12 Jan 2017 12:09:41 GMT

As most of you are probably already aware of .NET Core is the new lightweight open-source cross-platform development framework built by the .NET team. With .NET Core you can build applications that run on operating systems like Linux, Windows and MacOS as well as on the cloud. This is especially interesting for web applications that you can build with ASP.NET Core. In this blog post that I will split into 3 parts I would like to show you how easy it is to create your first ASP.NET Core application on the command line, then host it in a Linux based Docker container and then publish this container to Microsoft Azure.

Part 1: Create you first ASP.NET Core Application

Download .NET Core SDK and command line tools for Windows, Linux oder Mac from https://www.microsoft.com/net/core Open your command line / terminal to create your first ASP.NET Application:

dotnet new -t web

The type web (-t web) will create a ASP.NET Core MVC template web application. You can of course also use just "dotnet new" to create a basic .NET application and add everything necessary for a ASP.NET Core application yourself. Do a package restore to get all required packages for your template application from NuGet:

dotnet restore

Build and run you application (you can of course just build your application with "dotnet build"):

dotnet run

You can now see your application running locally on https://localhost:5000 Stop your local web server (Ctrl + C on Windows) and publish your application in release configuration:

dotnet publish -c Release

You can check if publishing worked correctly by changing to the "bin\Release\netcoreapp1.1\publish" folder of your project and start your app with "dotnet yourappname.dll". I had to first install Gulp globally on my machine as it is used to build the template application (npm install -g gulp). In the next blog post we will take our application, build a Docker image with it and host it in a Docker container. You can find Part 2 here. Links:

Download .NET Core: https://www.microsoft.com/net/core
Get started with ASP.NET Core: https://www.asp.net/get-started
NuGet Package Manager: https://www.nuget.org/
Docker: https://www.docker.com/
Microsoft Azure: https://azure.microsoft.com

Deployment der eigenen Azure Functions direkt aus dem lokalen Git Repository

Malte Lantin — Fri, 04 Nov 2016 16:16:45 GMT

Bekanntlich ist Serverless ja das neue Schwarz und auch Microsoft ist mit Azure Functions mittendrin. Jetzt können wir in Minuten, ohne jegliche Konfiguration von Servern und anderer Infrastruktur, unsere eigenen Dienste bereitstellen und zahlen nur für die tatsächliche Ausführungszeit. Azure Functions bietet zum Erstellen der eigenen Funktionen in C# oder NodeJS (weitere in Preview) einen Online Code Editor, welcher von Syntax-Highlighting bis direkter Buildintegration (im Falle von C#) die wichtigsten Funktionen mitbringe. Doch natürlich wollen wir auch weiterhin unsere üblichen (offline) Tools verwenden und Azure Functions in unsere Toolchain einbinden. Natürlich ist auch das möglich. Auch hier ist die Auswahl der Möglichkeiten groß, von einer direkten Anbindung an GitHub, bis zum Deployment über ein Push aus dem lokalen Git Repository. Und genau das wollen wir uns mal ansehen. Ich habe heute das schöne Tutorial von Manu (aka @CodePrincess) zum Erstellen eines Mock Servers auf Azure Functions durchlaufen, wollte das Deployment jedoch nicht automatisch von einem öffentlichen Repository, sondern manuell aus meinem lokalen Repository durchführen. Wie geht das also: 1. Nach dem Anlegen unsere Function App gehen wir links unten auf "Function app settings" 2. Dann klicken wir auf "Configure continuous integration" 3. Im sich öffnenden Menü ("Blade") klicken wir auf Setup. 4. Dann wählen wir "Local Git Repository" aus. Azure legt uns nun ein Git Repository im Context unserer Function App an. Der Inhalt des Repository spiegelt den Content meines 'Servers' wieder. Nun können wir das Repository klonen oder unsere Inhalte in dieses pushen. Eventuell müssen wir noch einen Benutzernamen und Passwort festlegen, falls wir das für diese Subscription noch nicht getan haben. 5. Für die Nutzung des Repositories brauchen wir die Git-URL. Diese finden wir über "Go to App Service Settings" und dann unter "Overview" oder "Properties" im Bereich "Settings" 6. Jetzt wo wir die URL haben, können wir unsere Inhalte veröffentlichen. 👌 Links:

@CodePrincess Serverless is the new black: https://medium.com/@codeprincess/serverless-is-the-new-black-1c11ce34a02a#.8r9uef4li
Azure Functions: https://azure.microsoft.com/en-us/services/functions/
Mock Server auf Azure Functions: https://medium.com/@codeprincess/get-your-funky-mock-server-7ca82ce9c35a#.6m52uina8

Microsoft Azure in Studium, Lehre und Forschung

Malte Lantin — Thu, 03 Nov 2016 08:52:13 GMT

Ich bin seit einigen Jahren häufig an Hochschulen in ganz Deutschland unterwegs und arbeite dort mit Studierenden, sowie Dozentinnen und Dozenten zusammen. Immer häufiger stellt sich hier die Frage nach einem Zugriff auf unsere Azure Cloud Plattform in Form von z.B. virtuellen Maschinen oder auch Azure Maschine Learning. Der Grund hierfür sind meist Lehrveranstaltungen, praktische Softwareentwicklung oder auch Forschungsprojekte. Aber auch Hintergrundinformationen und Lehrmaterialien rund um den praktischen Einsatz von Cloud-Technologien werden immer häufiger angefragt. Für beide Bereiche gibt es verschiedenen Angebote, die ich hier einmal an einer Stelle zusammenfassen möchte.

Nutzung von Microsoft Azure

Für Studierende bietet sich insbesondere die kostenfreie Nutzung über Microsoft Imagine (Aktivierung ohne Kreditkarte) sowie über die Visual Studio Dev Essentials (Aktivierung mit Kreditkarte) an. DreamSpark umfasst die Nutzung von ausgewählten Cloud-Diensten wie Azure App Services und SQL-Datenbanken. Bei den Visual Studio Dev Essentials steht ein monatliches Guthaben von 21 Euro für ein Jahr für alle Dienste von Microsoft Azure zur Verfügung. Für kürzere Projekte können wir auf persönliche Anfrage und abhängig von der Verfügbarkeit für Studierenden auch Microsoft Azure Pässe mit Guthaben und einer Laufzeit von einem Monat ausstellen. Des Weiteren haben Forscher über das Programm Azure for Research die Möglichkeit, Microsoft-Azure-Kontingente für Forschungsprojekte zu beantragen. Der Umfang des Research Grant richtet sich dabei nach dem Forschungsvorhaben und wird individuell vereinbart.

Lehrmaterialien

Im Rahmen unserer akademischen Programme stehen außerdem kostenlose Materialien und Unterlagen für Vorlesungen, Seminare und Übungen zur Verfügung. Diese können unabhängig von der Nutzung von Microsoft Azure in der Lehre verwendet werden. So können zum Beispiel Materialien zum Thema „Introduction to Cloud Computing“ unter aka.ms/lehre kostenfrei heruntergeladen werden. Weitere Unterlagen und Übungen für die Lehre im Bereich der Informatik finden Sie in unserem GitHub Repository. Im Bereich Complimentary Course Content stehen aktuell neben einer allgemeinen Einführung in das Thema „Cloud Computing“ auch Module in den Bereichen Webentwicklung, Cross-Platform Mobile Development, Internet of Things, DevOps sowie Data Analysis und Data Science zur Verfügung. Bei Feedback oder Anregungen zu den Inhalten, freuen wir uns jederzeit über Rückmeldungen.

[English] Host your own Minecraft Server on Microsoft Azure

Malte Lantin — Tue, 14 Jun 2016 19:26:55 GMT

You might already play Minecraft on your Windows / Linux PC or Mac, but once you want to play with your friends you will need your own Minecraft Server. You can either get your own Minecraft world for a monthly fee through Minecraft Realms or just host the server yourself (this only works with the Java based Windows, Linux, Mac Version of Minecraft). Setting up your own server is documented here, but as you will find there are some hoops to jump through. What if you could just use a preconfigured server, set it up for on demand usage and only pay when you are actually playing on your server? With Microsoft Azure you can do exactly that and you can even scale the size of your server (number of cpu cores, RAM, etc) when you need it. There are just a few steps to take when setting up the server (up to date as of June 14th 2016): 1. Get a Microsoft Azure account. You can start with a free trial and credits for your first month here: https://azure.microsoft.com/en-gb/free/ 2. Select the Minecraft Server Virtual Machine from the Azure Marketplace: https://azure.microsoft.com/en-gb/marketplace/partners/microsoft/minecraftserver/ 3. Click on "Create Virtual Machine" 4. Log in with your Microsoft Account and click on "Create" 5. Now you need to do some basic configuration work:

Pick a name for your server (the url for your server will be name.cloudapp.net)
Enter a username to use as an administrator
Define a password or upload an ssh key you will use to access your server
Select a server size / pricing tier, A1 will be a good size to start with
Optional: Configure Network, Storage and Diagnostics
Optional: Select or define a name to group your Azure ressources
Optional: Select the subscription to use if you have got multiple
Select a location / data center to host your server, pick a location close to your users

6. Now click on "Create" to have Azure create you Minecraft Server 7. After a few minutes your server will be ready. To connect to this server in Minecraft just use the URL and Port 25565 like this: yourname.cloudapp.net:25565 8. If you use a current version of Minecraft and try this right away, you will get the error message that your Minecraft server version needs to be updated from 1.8 to a current version. 9. To update your server (and do some additional settings), log into your server using ssh (Linux, Mac) or a tool like Putty (Windows). Bash and SSH will come to Windows soon. 10. After logging into your server with you username and password first switch to the folder of your Minecraft Server "cd /srv/minecraft_server". 11. Download the current version of the Minecraft server using CURL. Currently this would be "sudo curl --remote-name https://s3.amazonaws.com/Minecraft.Download/versions/1.10/minecraft_server.1.10.jar". You can find an up to date link for downloading the current server version on https://minecraft.net/en/download/server. 12. While in the correct folder let's also update ops.json file right now, so that we have got some rights to modify the game when logged into our server. We can edit the file using using nano like this: "sudo nano ops.json". Edit the file like shown below using your (or your friends) Minecraft user name and unique ID. You can find out the unique ID using the the tool on https://mcuuid.net. [ { "uuid": "uuid", "name": "minecraft_username", "level": 4 } ] 13. Now save the file using "CTRL + o" and exit Nano using "CTRL + x". You can also edit your "server.properties" file here to do some further configuration of your server. You can find more details about the server properties in the Minecraft Wiki. 14. Let us now tell the Minecraft server service to use the current version of Minecraft we just downloaded. For that we will edit the service configuration using Nano like this: "sudo nano /etc/systemd/system/minecraft-server.service". We just need to reference the new version of the Minecraft Server here. 15. Save the file and exit Nano. 16. Now stop and restart the the Minecraft server service. You might need to reload the systemd state before using "systemctl daemon-reload". Now run "systemctl restart minecraft-server" to restart the service. 17. You will need to give the service a few seconds to restart. After that you can refresh your server in you Minecraft client and connect to your server. 18. Congratulations! You have now set up your own Minecraft Server. 19. To make the Minecraft server service start automatically after you shutdown / start or reboot your server through the Azure Management Portal use the following command: "sudo ln -s /etc/systemd/system/minecraft-server.service /etc/systemd/system/multi-user.target.wants/minecraft-server.service" Use the Azure Portal (https://portal.azure.com) to stop your server when you don't use it. You can restart it when ever you need it. When your server is stopped through the Azure Portal it does not cause any VM charges. You just have to pay a small amount to keep your server drive persisted in Azure storage. You can use the calculator on the Azure website to calculate your charges. Keep in mind that charges for runnig your server depend on the size of you virtual machine and are charged per minute. So just make sure your server is in the "deallocated" state when you don't use it. Have fun with your new Minecraft server! Links:

Minecraft Website: https://minecraft.net
Minecraft Realms: https://minecraft.net/en/realms/pc/
Minecraft Wiki - Setting up a server: https://minecraft.gamepedia.com/Tutorials/Setting_up_a_server
Microsoft Azure Trial: https://azure.microsoft.com/en-gb/free/
Minecraft Server Azure Virtual Machine: https://azure.microsoft.com/en-gb/marketplace/partners/microsoft/minecraftserver/
Minecraft Server Download: https://minecraft.net/en/download/server
Minecraft Unique ID: https://mcuuid.net
Minecraft Wiki Server Configuration: https://minecraft.gamepedia.com/Server.properties
Azure Pricing Calculator: https://azure.microsoft.com/en-us/pricing/calculator/

Neuer Virtual Academy Kurs - Eine Einführung in PowerApps

Malte Lantin — Mon, 04 Apr 2016 08:19:01 GMT

Noch vor Ostern ist mein neuer Microsoft Virtual Academy (MVA) Kurs zu PowerApps erschienen. PowerApps ist eine Sammlung von Werkzeugen, die es Fachanwendern erlaubt in einfacher Art und Weise Apps für Smartphones, Tablets (iOS, Windows) sowie Windows 10 PCs zu erstellen. Gearbeitet wird dabei in einer für Office-Anwender vertrauten Umgebung, welche nur geringe bis keine Programmierkenntnisse erfordert. Die erstellten Apps können auf bereits im Unternehmen vorhandene Daten zurückgreifen und damit sowohl neue Anwendungsfälle abdecken, als auch existierende Systeme um eine mobile Komponente ergänzen. Gemeinsam mit von der Unternehmens IT bereitsgestellten Schittstellen und Diensten sowie den mitgelieferten Schnittstellen bietet PowerApps damit eine einfach zu nutzende Möglichkeit Geschäftanwendungen für mobile Endgeräte zu erstellen und diese mit anderen Anwendern im Unternehmen zu teilen. In vier Modulen stelle ich die Technologie in diesem Kurs vor. Links:

MVA Kurs - Eine Einführung in PowerApps: https://mva.microsoft.com/de-de/training-courses/eine-einfhrung-in-powerapps-16124
Microsoft Virtual Academy (MVA): https://mva.microsoft.com/
PowerApps Website: https://powerapps.microsoft.com