Kategorie: Microsoft Azure

Digitizing trust: Azure Blockchain Service simplifies blockchain development

In a rapidly globalizing digital world, business processes touch multiple organizations and great sums are spent managing workflows that cross trust boundaries. As digital transformation expands beyond the walls of one company and into processes shared with suppliers, partners, and customers, the importance of trust grows with it. Microsoft’s goal is to help companies thrive in this new era of secure multi-party computation by delivering open, scalable platforms, and services that any company from game publishers and grain processors, to payments ISVs and global shippers can use to digitally transform the processes they share with others.

Azure Blockchain Service: The foundation for blockchain applications in the cloud

Azure Blockchain Service is a fully-managed blockchain service that simplifies the formation, management, and governance of consortium blockchain networks so businesses can focus on workflow logic and application development. Today, we’re excited to announce that the public preview is now available.

With a few simple clicks, users can create and deploy a permissioned blockchain network and manage consortium policies using an intuitive interface in the Azure portal. Built-in governance enables developers to add new members, set permissions, monitor network health and activity, and execute governed, private interactions through integrations with Azure Active Directory.

This week, we also announced an exciting partnership with J.P. Morgan to make Quorum the first ledger available in Azure Blockchain Service. Because it’s built on the popular Ethereum protocol, which has the world’s largest blockchain developer community, Quorum is a natural choice. It integrates with a rich set of open-source tools while also supporting confidential transactions, something our enterprise customers require. Quorum customers like Starbucks, Louis Vuitton, and our own Xbox Finance team can now use Azure Blockchain Service to quickly expand their networks with lower costs, shifting their focus from infrastructure management to application development and business logic.

“We are incredibly proud of the success Quorum has had over the last four years as organizations around the world use Quorum to solve complex business and societal problems. We are delighted to partner alongside Microsoft as we continue to strengthen Quorum and expand capabilities and services on the platform.”

— Umar Farooq, Global Head of Blockchain at J.P. Morgan

We’re excited to offer customers an enterprise-grade Ethereum stack with Quorum, and look forward to adding new capabilities to Azure Blockchain Service in the coming months, including digital token management, improved application integration, and support for R3’s Corda Enterprise.

An application-driven approach

The ledger is just the foundation for new applications. After configuring the underlying blockchain network with Azure Blockchain Service, you need to codify your business logic using smart contracts. Until now, this has been cumbersome, requiring multiple command-line tools and limited developer IDE integration. Today we are releasing an extension for VS Code to address these issues. This extension allows you to create and compile Ethereum smart contracts, deploy them to either the public chain or a consortium network in Azure Blockchain Service, and manage their code using Azure DevOps.

Once your network is created and smart contract state machines are deployed, you must build an application in order for consortium participants to share business logic and data represented by the smart contracts. A key challenge has been integrating these applications with smart contracts so they either respond to smart contract updates or execute smart contract transactions. This connects business processes managed in other systems such as databases, CRM, and ERP systems with the ledger. Our new Azure Blockchain Dev Kit makes this easier than ever with connectors and templates for Logic Apps and Flow as well as integrations with serverless tools like Azure Functions.

You can learn more about how to build your first network, code your smart contracts, and interact with the ledger in the latest episodes of the web series Block Talk.

Embracing open communities

Over the past year, we have been preparing our Confidential Consortium Framework (CCF) for public release. CCF uses trusted execution environments (TEEs) such as SGX and VSM to enable ledgers that integrate with it to execute confidential transactions with the throughput and latency of a centralized database. Confidentiality and high performance are key requirements of our enterprise customers. We’re excited to announce that we have finished the first version of CCF, integrated with Quorum, and have made the source code available on Github.

Microsoft believes that the best way to bring blockchain to our customers is by partnering with the diverse and talented open source communities that are driving blockchain innovation today. We began this journey in 2015, partnering with the growing communities around Ethereum, R3 Corda, and Hyperledger to make those technologies available in Azure. Instead of building our own ledger, or creating a ledger alternative, we have worked to make open source technology developers love and work better with Azure. All of the tooling released this week allows developers to work against both consortium networks in Azure Blockchain Service and with public Ethereum.

“Microsoft has embraced the open community of blockchain developers and has brought the best of their cloud development tooling to the developers building the next wave of decentralized applications. With Azure Blockchain Service and Ethereum integrations for tools like VS Code, Microsoft is demonstrating its commitment to open blockchain development.”

— Vitalik Buterin, co-founder of Ethereum

Next steps

Learn more about Azure Blockchain Service and get started today:

Create a free Azure account and get $200 to build your first network.
See how to configure and deploy your network, author smart contracts, and interact with the ledger on the web series Block Talk.
Accelerate development with code samples and tutorials in our Azure Blockchain Dev Kit.
Learn how you can use blockchain and databases like Azure Cosmos DB together.

Quelle: Azure

Azure Stack IaaS – part seven

If you do it often, automate it

In the virtualization days, before cloud and self-service, it took a while to get all the approvals, credentials, virtual LANs (VLANs), logical unit numbers (LUNs), etc. It took so long, that the actual creation part was easy. When cloud came along with self-service, not only was it easier to create a virtual machine (VM) without relying on others, but it changed our thinking about whether VMs were precious or disposable. At the same time developers were moving to a world of continuous delivery to serve their customers who expect apps with a constant stream of new features. This is the reason all real clouds provide automation APIs to quickly create VMs and other resources, including the infrastructure they rely on. This is often called “Infrastructure as code.” Azure’s API is governed by the Azure Resource Manager (ARM). When you set up Azure Stack, you get your own private instance of ARM.

In this blog post I will cover the automation options in your Cloud IaaS toolkit.

Azure portal

The best place to learn this is from first completing a VM deployment through the portal. Azure Stack provides the same portal as Azure. When you get to the last confirmation page, click the Download Template link. This will show you the template that will be used to deploy the VM you just specified on the previous screens.

As you look through the template – which is in JSON format – you can see how the virtual machine, network, and storage is defined. You’ll see an OS profile and hardware profile for your VM. Using this template, you could deploy this same VM over and over. This is perfect if you’re helping your developers with Continuous Integration and Delivery (CI/CD).

One thing about an ARM template is it is not a procedural script like you might get with standard automation tools. This template creates the resources as a single deployment transaction. ARM will either get to the goal state or the deployment will fail. If it fails, you have a chance to fix failure condition and move forward or delete the deployment and start over. This way you don’t get something other than what you specify.

Once you save your template, you can redeploy it in the portal, using the Template deployment item in the marketplace:

Learn more:
Azure Resource Manager overview
Quickstart: Create templates using the Azure Portal
Deploy resources in the portal using a custom template

Visual Studio and Visual Studio Code

Visual Studio can be scary for an infrastructure person, but if you can master some simple things, you can really help your team down the road of automation. The biggest thing that Visual Studio provides is the real-time feedback that you’re authoring the ARM JSON template correctly in terms of syntax. You don’t get this in Notepad. Get started by creating a new Azure Resource Group project:

A great way to start authoring a template is to use Quickstart templates. Azure Stack has a number of Quickstart templates you can use. When you start your new project in Select Azure Template, pick Azure Stack Quickstart from the drop-down list.

Visual Studio not only helps you author the template, it allows you to deploy the template directly to Azure Stack. When you sign into Visual Studio, all of your subscriptions in both Azure and Azure Stack show up as deployment targets. Since I use a number of Azure Stack environments, I have lots of deployment options:

Another way to create these templates is in Visual Studio Code. The ARM template is a JSON file, so you need a good lightweight authoring tool to work on the JSON file. Visual Studio Code (VS Code) is a great option.

After installing VS Code, you need to add the Azure Resource Manager Tools extension. This extension adds many features that simplify template authoring that help you manage variables, parameters, and resource blocks with features like including formatting and color coding. Check it out in the image below:

Learn more:
Install Visual Studio and Connect to Azure Stack
Deploy templates to Azure Stack using Visual Studio
Create a template in Visual Studio
Create a template in Visual Studio Code

PowerShell and Azure command-line interface

The Portal and Visual Studio are both deployment options for your template. PowerShell and the Azure command-line interface (CLI) are two other options. Since Azure Stack is your own private instance of the Azure Resource Manager, you need to connect to your unique instance.

To connect to Azure Stack with PowerShell, use the Add-AzureRMEnvironment cmdlet, specifying the ARM endpoint for your environment. Depending on how your Azure Stack environment has been set up, you will either authenticate using your Azure Active Directory credentials or your organization’s Azure Directory (referred to in the documentation as ADFS).

Azure CLI is Microsoft's cross-platform command-line experience for managing Azure resources. It works on Mac, Linux, and Windows. In Azure you can run the CLI in Cloud Shell. You can use Azure CLI to connect to Azure Stack and deploy IaaS templates. Just like PowerShell, you need to first connect to your Azure Stack’s unique ARM endpoint. For CLI you use the az cloud register command. To deploy your ARM template you use the az group deployment create command.

Please note: We have not implemented Cloud Shell on Azure Stack yet. Cloud Shell allows you to run the Azure CLI directly inside your browser. If you would like to see this, make sure you put in a vote on Azure Cloud Shell UserVoice.

Learn more:
Install PowerShell for Azure Stack
Connect to Azure Stack with PowerShell
Deploy in Azure Stack with PowerShell
Use Azure CLI on Azure Stack
Deploy Azure Resource Manager Templates with Azure CLI

The cloud is for automation

The more people use clouds like Azure and Azure Stack, the less they use the portal and the more they use automation. There is an automation option in Azure and Azure Stack for all your needs. Say goodbye to virtualization and say hello to Cloud IaaS with your automation toolkit.

In this blog series

We hope you come back to read future posts in this blog series. Here are some of our past and upcoming topics:

Azure Stack at its core is an Infrastructure-as-a-Service (IaaS) platform
Start with what you already have
Fundamentals of IaaS
Protect your stuff
Do it yourself
Pay for what you use
Build on the success of others
Journey to PaaS

Quelle: Azure

Deploy a FHIR sandbox in Azure

This blog post was authored by Michael Hansen, Senior Program Manager, Microsoft Azure.

In connection with HIMSS 2019, we announced the Azure API for FHIR, which provides our customers with an enterprise grade, managed FHIR® API in Azure. Since then, we have been busy improving the service with new configuration options and features. Some of the features we have been working on include authentication configuration and the SMART on FHIR Azure Active Directory Proxy, which enable the so-called SMART on FHIR EHR launch with the Azure API for FHIR.

We have developed a sandbox environment that illustrates how the service and the configuration options are used. In this blog post, we focus on how to deploy the sandbox in Azure. Later blog posts will dive into some of the technical details of the various configuration options.

The Azure API for FHIR team maintains a GitHub repository with sample applications. It is maintained by the product engineering team to ensure that it works with the latest features of the Azure API for FHIR. The repository contains a patient dashboard application, Azure Function that will load patient data generated with Synthea, and example templates for SMART on FHIR applications:

Deployment instructions

The repository contains fully automated PowerShell scripts that you can use to deploy the sandbox scenario. The deployment script will create Azure Active Directory application registrations and a test user. If you do not want to create these Azure Active Directory objects in the tenant associated with your Azure subscription, we recommend you create a separate Azure Active Directory tenant to use for data plane access control.

The deployment script is written for PowerShell and uses the AzureAd PowerShell module. If you don’t have access to PowerShell on your computer, you can use the Azure Cloud Shell. In the cloud shell, you can deploy the sandbox environment with:

# Clone source code repository
cd $HOME
git clone https://github.com/Microsoft/fhir-server-samples
cd fhir-server-samples/deploy/scripts

# Log in to Azure AD:
Connect-AzureAd -TenantDomain <mytenantdomain>.onmicrosoft.com

# Connect to Azure Subscription
Login-AzureRmAccount

# Selection subscription
Select-AzureRmSubscription -SubsciptionName “Name of your subscription”

# Deploy Sandbox
.Create-FhirServerSamplesEnvironment.ps1 -EnvironmentName <NameOfEnvironment> -EnvironmentLocation westus2 -AdminPassword $(ConvertTo-SecureString -AsPlainText -Force "MySuperSecretPassword")

It will take around 5 minutes to deploy the environment. The deployment script will create a resource group with the same name as the environment. In there, you will find all the resources associated with the sandbox.

Loading synthetic data

The environment resource group will contain a storage account with a container named “FhirImport.” If you upload Synthea patient bundles to this storage account, they will be ingested.

Using the patient dashboard

There are two versions of the patient dashboard, they can be located at:

https://<NameOfEnvironment>dash.azurewebsites.net: This is an ASP.NET patient dashboard. The GitHub repository contains the source code for this patient dashboard.
https://<NameOfEnvironment>js.azurewebsites.net: This is a single page JavaScript application. The source code is also in the GitHub repository.

When you navigate to either of those URLs, you will be prompted to log in. The administrator user is created by the deployment script and will have the username <NameOfEnvironment>-admin@<mytenantdomain>.onmicrosoft.com and the password is whatever you chose it to be during deployment. If you have uploaded some patients using the Synthea uploader, you should be able to display a list of patients. This shows the view in the JavaScript dashboard.

You can click details on a specific patient to get more information:

You can also use the links for the SMART on FHIR applications to get the growth chart application this patient:

The sandbox provides other useful tools. As an example, the “About me” link will provide you with details about the FHIR endpoint including a token that can be used to access the FIR API using tools like Postman.

Deleting the sandbox

When you are done exploring the Azure API for FHIR and the FHIR sandbox, it is easily deleted with:

.Delete-FhirServerSamplesEnvironment.ps1 -EnvironmentName <NameOfEnvironment>

FHIR® is the registered trademark of HL7 and is used with the permission of HL7
Quelle: Azure

Building recommender systems with Azure Machine Learning service

Recommendation systems are used in a variety of industries, from retail to news and media. If you’ve ever used a streaming service or ecommerce site that has surfaced recommendations for you based on what you’ve previously watched or purchased, you’ve interacted with a recommendation system. With the availability of large amounts of data, many businesses are turning to recommendation systems as a critical revenue driver. However, finding the right recommender algorithms can be very time consuming for data scientists. This is why Microsoft has provided a GitHub repository with Python best practice examples to facilitate the building and evaluation of recommendation systems using Azure Machine Learning services.

What is a recommendation system?

There are two main types of recommendation systems: collaborative filtering and content-based filtering. Collaborative filtering (commonly used in e-commerce scenarios), identifies interactions between users and the items they rate in order to recommend new items they have not seen before. Content-based filtering (commonly used by streaming services) identifies features about users’ profiles or item descriptions to make recommendations for new content. These approaches can also be combined for a hybrid approach.

Recommender systems keep customers on a businesses’ site longer, they interact with more products/content, and it suggests products or content a customer is likely to purchase or engage with as a store sales associate might. Below, we’ll show you what this repository is, and how it eases pain points for data scientists building and implementing recommender systems.

Easing the process for data scientists

The recommender algorithm GitHub repository provides examples and best practices for building recommendation systems, provided as Jupyter notebooks. The examples detail our learnings on five key tasks:

Data preparation – Preparing and loading data for each recommender algorithm
Modeling – Building models using various classical and deep learning recommender algorithms such as Alternating Least Squares (ALS) or eXtreme Deep Factorization Machines (xDeepFM)
Evaluating – Evaluating algorithms with offline metrics
Model selection and optimization – Tuning and optimizing hyperparameters for recommender models
Operationalizing – Operationalizing models in a production environment on Azure

Several utilities are provided in reco utils to support common tasks such as loading datasets in the format expected by different algorithms, evaluating model outputs, and splitting training/test data. Implementations of several state-of-the-art algorithms are provided for self-study and customization in an organization or data scientists’ own applications.
In the image below, you’ll find a list of recommender algorithms available in the repository. We’re always adding more recommender algorithms, so go to the GitHub repository to see the most up-to-date list.

 

Let’s take a closer look at how the recommender repository addresses data scientists’ pain points.

It’s time consuming to evaluate different options for recommender algorithms

One of the key benefits of the recommender GitHub repository is that it provides a set of options and shows which algorithms are best for solving certain types of problems. It also provides a rough framework for how to switch between different algorithms. If model performance accuracy isn’t enough, an algorithm better suited for real-time results is needed, or the originally chosen algorithm isn’t the best fit for the type of data being used, a data scientist may want to switch to a different algorithm.

Choosing, understanding, and implementing newer models for recommender systems can be costly

Selecting the right recommender algorithm from scratch and implementing new models for recommender systems can be costly as they require ample time for training and testing as well as large amounts of compute power. The recommender GitHub repository streamlines the selection process, reducing costs by saving data scientists time in testing many algorithms that are not a good fit for their projects/scenarios. This, coupled with Azure’s various pricing options, reduces data scientists’ costs on testing and organization’s costs in deployment.

Implementing more state-of-the-art algorithms can appear daunting

When asked to build a recommender system, data scientists will often turn to more commonly known algorithms to alleviate the time and costs needed to choose and test more state-of-the-art algorithms, even if these more advanced algorithms may be a better fit for the project/data set. The recommender GitHub repository provides a library of well-known and state-of-the-art recommender algorithms that best fit certain scenarios. It also provides best practices that, when followed, make implementing more state-of-the-art algorithms easier to approach.

Data scientists are unfamiliar with how to use Azure Machine Learning service to train, test, optimize, and deploy recommender algorithms

Finally, the recommender GitHub repository provides best practices for how to train, test, optimize, and deploy recommender models on Azure and Azure Machine Learning (Azure ML) service. In fact, there are several notebooks available on how to run the recommender algorithms in the repository on Azure ML service. Data scientists can also take any notebook that has already been created and submit it to Azure with minimal or no changes.

Azure ML can be used intensively across various notebooks for tasks relating to AI model development, such as:

Hyperparameter tuning
Tracking and monitoring metrics to enhance the model creation process
Scaling up and out on compute like DSVM and Azure ML Compute
Deploying a web service to Azure Kubernetes Service
Submitting pipelines

Learn more

Utilize the GitHub repository for your own recommender systems.

Learn more about the Azure Machine Learning service.

Get started with a free trial of Azure Machine Learning service.
Quelle: Azure