03

Aug

8/16/18 Webinar: Building a KPI Scorecard with Custom Visuals in Power BI with Reid Havens

https://powerbi.microsoft.com/en-us/blog/8-16-18-webinar-building-a-kpi-scorecard-with-custom-visuals-in-power-bi-with-reid-havens/Source: https://powerbi.microsoft.com/en-us/blog/8-16-18-webinar-building-a-kpi-scorecard-with-custom-visuals-in-power-bi-with-reid-havens/           Reid Havens covers cover building a KPI scorecard with custom visuals in Power BI. Learn about some great custom visuals in conjunction with DAX calculations to build out a quality scorecard template to READ MORE

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03

Aug

General availability of instance size flexibility for Azure Reserved Virtual Machine Instances

This blog post was co-authored by Arabinda Mohapatra, Prinicipal Engineering Manager, Microsoft Azure.

We are excited to announce instance size flexibility for Azure Reserved Virtual Machine Instances, a new feature that makes your reserved instance purchasing and management even simpler by applying reservation discounts to different virtual machine (VM) sizes within the same VM group.

With instance size flexibility, you don’t have to deploy the exact same VM size to get the benefit of your purchased Azure Reserved Instances (RI) as other VM sizes within the same VM group also get the RI discount.

For example, consider the following VM groups:

VM name VM group Ratios

Standard_D2s_v3

DSv3 Series

1

Standard_D4s_v3

DSv3 Series

2

Standard_D8s_v3

DSv3 Series

4

Standard_D16s_v3

DSv3 Series

8

Standard_D32s_v3

DSv3 Series

16

Standard_D64s_v3

DSv3 Series

32

If you have purchased 1 Azure Reserved Instance for a D2s_v3 VM, then the following VM Instances could be covered through your reserved instance purchase if they are in the same region:

1 Standard_D2S_v3 1/2

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03

Aug

General availability of instance size flexibility for Azure Reserved Virtual Machine Instances

This blog post was co-authored by Arabinda Mohapatra, Prinicipal Engineering Manager, Microsoft Azure.

We are excited to announce instance size flexibility for Azure Reserved Virtual Machine Instances, a new feature that makes your reserved instance purchasing and management even simpler by applying reservation discounts to different virtual machine (VM) sizes within the same VM group.

With instance size flexibility, you don’t have to deploy the exact same VM size to get the benefit of your purchased Azure Reserved Instances (RI) as other VM sizes within the same VM group also get the RI discount.

For example, consider the following VM groups:

VM name VM group Ratios

Standard_D2s_v3

DSv3 Series

1

Standard_D4s_v3

DSv3 Series

2

Standard_D8s_v3

DSv3 Series

4

Standard_D16s_v3

DSv3 Series

8

Standard_D32s_v3

DSv3 Series

16

Standard_D64s_v3

DSv3 Series

32

If you have purchased 1 Azure Reserved Instance for a D2s_v3 VM, then the following VM Instances could be covered through your reserved instance purchase if they are in the same region:

1 Standard_D2S_v3 1/2

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02

Aug

Distributed Deep Learning on AZTK and HDInsight Spark Clusters
Distributed Deep Learning on AZTK and HDInsight Spark Clusters

This post is authored by Chenhui Hu, Data Scientist at Microsoft.

Deep learning has achieved great success in many areas recently. It has attained state-of-the-art performance in applications ranging from image classification and speech recognition to time series forecasting. The key success factors of deep learning are – big volumes of data, flexible models and ever-growing computing power.

With the increase in the number of parameters and training data, it is observed that the performance of deep learning can be improved dramatically. However, when models and training data get big, they may not fit in the memory of a single CPU or GPU machine, and thus model training could become slow. One of the approaches to handle this challenge is to use large-scale clusters of machines to distribute the training of deep neural networks (DNNs). This technique enables a seamless integration of scalable data processing with deep learning. Other approach like using multiple GPUs on a single machine works well with modest data but could be inefficient for big data.

Although the term “distributed deep learning” may sound scary if you’re hearing it for the first time, through this blog post, I show how you can quickly write scripts to

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02

Aug

Real example: improve accuracy, reduce training times for existing R codebase

When you buy an item on a favored website, does the site show you pictures of what others have bought? That’s the result of a recommendation system. Retailers have been building such systems for years, many built using the programming language R. For older implementations of recommender systems, it’s time to consider improving performance and scalability by moving these systems to the cloud —the Azure cloud.

Problem: to re-host and optimize an existing R model in Azure

Recently, we were asked to help a customer improve the performance and process surrounding the R implementation of their recommender solution and host the model in Azure. Many of their early analytic products were built in R, and they wanted to preserve that investment. After a review of their solution, we identified bottlenecks that could be vanquished. We worked together to find a way to significantly improve the model training time using parallel R algorithms. Then we worked to streamline how they operationalized their R model. All the work was done using libraries available with Microsoft Machine Learning Server (R Server). 

The architecture: Azure SQL + Machine Learning Server

There are several components and steps to the solution. We needed a database

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02

Aug

Real example: improve accuracy, reduce training times for existing R codebase

When you buy an item on a favored website, does the site show you pictures of what others have bought? That’s the result of a recommendation system. Retailers have been building such systems for years, many built using the programming language R. For older implementations of recommender systems, it’s time to consider improving performance and scalability by moving these systems to the cloud —the Azure cloud.

Problem: to re-host and optimize an existing R model in Azure

Recently, we were asked to help a customer improve the performance and process surrounding the R implementation of their recommender solution and host the model in Azure. Many of their early analytic products were built in R, and they wanted to preserve that investment. After a review of their solution, we identified bottlenecks that could be vanquished. We worked together to find a way to significantly improve the model training time using parallel R algorithms. Then we worked to streamline how they operationalized their R model. All the work was done using libraries available with Microsoft Machine Learning Server (R Server). 

The architecture: Azure SQL + Machine Learning Server

There are several components and steps to the solution. We needed a database

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02

Aug

Azure SQL Vulnerability Assessment – now with PowerShell support!

https://blogs.msdn.microsoft.com/sqlsecurity/2018/08/02/azure-sql-vulnerability-assessment-now-with-powershell-support/Source: https://blogs.msdn.microsoft.com/sqlsecurity/2018/08/02/azure-sql-vulnerability-assessment-now-with-powershell-support/   You can now manage your SQL Vulnerability Assessments at scale using the new SQL VA PowerShell cmdlets. The cmdlets can be found in the Azure Resource Manager module, AzureRM 6.6.0, within the AzureRM.Sql package. Take a look READ MORE

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02

Aug

Managed Disks migration now available in the Azure Portal

We are excited to announce the capability of converting VMs with unmanaged Disks to Managed Disks in the Azure Portal! Now you can migrate to Managed Disks in single click without requiring PowerShell or CLI scripts.

Our customers love the benefits of using Managed Disks. Many customers have already adopted Managed Disks since we launched it. If you have not started using Managed Disks, here’s a quick recap of all the capabilities to motivate you to use Managed Disks.

Scale your application without worrying about storage account limits. Achieve high-availability across your compute and storage resources with aligned fault domains. Create VM Scale Sets with up to 1,000 instances. Integrate disks, snapshots, images as first-class resources into your architecture. Secure your disks, snapshots, and images through Azure Role Based Access Control (RBAC)

To read more about the benefits of Managed Disks, see Azure Managed Disks Overview.

Migrating to Managed Disks in Azure Portal

Migrating in the Azure Portal is a pretty simple experience. Let’s walk through this process.

If you are using a VM with unmanaged disks, you will see an info banner on the VM overview blade.

Once you click on the banner, it will launch the migration

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02

Aug

Improve collaborative care and clinical data sharing with blockchain

Currently, the healthcare industry suffers major inefficiencies due to diverse uncoordinated and unconnected data sources/systems. Collaboration is vital to improve healthcare outcomes. With digitized health data, the exchange of healthcare information across healthcare organizations is essential to support effective care collaboration. Traditional health information exchanges have had limited success.

Blockchain offers new capabilities to greatly improve health information exchange. At Microsoft we are working to maximize the benefits of solutions that have the potential to improve patient outcomes, reduce healthcare costs, and enhance the experiences of patients and healthcare workers. With that I’d like to announce a new partner solution and pilot for a better health information exchange that uses blockchain, and runs in Microsoft Azure.

Interoperability and exchanges

Grapevine World, one of the leaders in the application of blockchain technology, make use of the Institute for Healthcare Enterprise (IHE) methodology for interoperability. They employ multiple blockchains for tracking data provenance, and provide a crypto token as means of exchange within their ecosystem.

Grapevine World is a decentralized ecosystem for the seamless exchange and utilization of health data in a standardized, secure manner. In collaboration with the University of Southampton and Tiani Spirit, they have developed a new blockchain-enabled

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02

Aug

Getting started with IoT: what do you do with all that data?

The Internet of Things (IoT) is all about generating data, helping organizations discover new insights about their operations from that data, and identifying opportunities to become more productive and innovative. In the first post in this series, we introduced Getting started with IoT: how to connect, secure, and manage your “things”. But once your IoT devices are deployed, secured, and provisioned through Azure IoT Hub, the question remains: where do you send all of the data?

Information such as the telemetry data generated by your devices and sensors is time-sensitive. Other information isn’t. The role of Azure IoT Hub is to determine how each data packet needs to be prioritized and where to send it. These messages fall into four general categories:

IoT Hub message routing: Includes alerts and time sensitive telemetry data File uploads: Media files and large batches of telemetry data that are uploaded by intermittently connected devices, or compressed to conserve bandwidth Device Twin reported properties: Device state information such as capabilities and conditions, or the status of workflows like firmware or configuration updates IoT Hub integration with Event Grid: An alternative to message routing, Event Grid integrates IoT Hub Events into Azure and non-Azure services. These

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