Accelerate your AI applications with Azure NC A100 v4 virtual machines

Real-world AI has revolutionized and changed how people live during the past decade, including media and entertainment, healthcare and life science, retail, automotive, finance service, manufacturing, and oil and gas. Speaking to a smart home device, browsing social media with recommended content, or taking a ride with a self-driving vehicle is no longer in the future. With the ease of your smartphone, you can now deposit checks without going to the bank? All of these advances have been made possible through new AI breakthroughs in software and hardware.

At Microsoft, we host our deep learning inferencing, cognitive science, and our applied AI services on the NC series instances. The learnings and advancements made in these areas with regard to our infrastructure are helping drive the design decisions for the next generation of NC system. Because of our approach, our Azure customers are able to benefit from our internal learnings.

We are pleased to announce that the next generation of NC A100 v4 series is now available for preview. These virtual machines (VMs) come equipped with NVIDIA A100 80GB Tensor Core PCIe GPUs and 3rd Gen AMD EPYC™ Milan processors. These new offerings improve the performance and cost-effectiveness of a variety of GPU performance-bound real-world AI training and inferencing workloads. These workloads cover object detection, video processing, image classification, speech recognition, recommender, autonomous driving reinforcement learning, oil and gas reservoir simulation, finance document parsing, web inferencing, and more.

The NC A100 v4-series offers three classes of VM ranging from one to four NVIDIA A100 80GB PCIe Tensor Core GPUs. It is more cost-effective than ever before, while still giving customers the options and flexibility they need for their workloads.

Size	vCPU	Memory (GB)	GPUs (NVIDIA A100 80 GB Tensor Core)	Azure Network (Gbps)
Standard_NC24ads_A100_v4	24	220	1	20
Standard_NC48ads_A100_v4	48	440	2	40
Standard_NC96ads_A100_v4	96	880	4	80

Compared to the previous NC generation (NCv3) with NVIDIA Volta architecture-based GPUs, customers will experience between 1.5 and 2.5 times the performance boost due to:

◉ Two times GPU to host bandwidth.

◉ Four times vCPU cores per GPU VM.

◉ Two times RAM per GPU VM.

◉ Seven independent GPU instances on a single NVIDIA A100 GPU through Multi-Instance GPU (MIG) on Linux OS.

Below is a sample of what we experienced while running ResNet50 AI model training across a variety of batch sizes using the VM size NC96ads_A100_v4 compared to the existing NCv3 4 V100 GPUs VM size NC24s_v3. Tests were conducted across a range of batch sizes, from one to 256.

Figure 1: ResNet50 results were generated using NC24s_v3 and NC96ads_A100_v4 virtual machine sizes.

With our latest addition the NC series, you can reduce the time it takes to train your model training in around half the time and still within budget. You can seamlessly apply the trained cognitive science models to applications through batch inferencing, run multimillion atomics biochemistry simulations for next-generation medicine, host your web and media services in the cloud for tens of thousands of end-users, and so much more.

Source: microsoft.com

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Now in preview: Azure Virtual Machines with Ampere Altra Arm-based processors

Up to 50 percent better price-performance than comparable x86-based virtual machines (VMs) for scale-out workloads.

The demand for compute capacity to sustain business modernization and digital transformation initiatives continues to grow. Organizations are facing a complex set of challenges as they deploy a broad range of workloads globally, from the edge to the cloud. There is also a need for a new breed of operationally efficient cloud-native computing solutions that can meet this demand without a massive growth in infrastructure footprint and energy consumption.

To address some of these challenges Microsoft is announcing the preview of Azure Virtual Machines series featuring the Ampere Altra Arm-based processor. The new VMs are engineered to efficiently run scale-out workloads, web servers, application servers, open-source databases, cloud-native as well as rich .NET applications, Java applications, gaming servers, media servers, and more. The new VM series include general-purpose Dpsv5 and memory-optimized Epsv5 VMs, which can deliver up to 50 percent better price-performance than comparable x86-based VMs. You can request access to the preview by filling out this form.

The new Azure Virtual Machines, featuring the Ampere Altra Arm-based processor, further extend our portfolio of compute solutions to help customers manage complexity and seamlessly run modern, dynamic, and scalable applications. Azure customers will benefit from the improvements the new VMs provide in terms of scalability, performance, and operational efficiency.

One customer is Amadeus, the leading IT provider for the global travel industry. Their research and development team gained early access to the preview and is excited about the potential of the offering.

“We power better journeys through travel technology. To achieve that, we design and deliver the most complex, trusted, and critical systems that our customers need”, said Denis Lacroix, SVP Cloud Transformation Program at Amadeus. “Travelers demand that their needs are met efficiently and quickly, and that they receive a consistent, personalized experience through every step of their journeys, from inspiration to search and booking, to ticketing, check-in, and arriving home. With Azure Arm64 VMs, we will be able to deliver higher throughput and even better experiences than the x86 VM that we’ve used in the past. Azure Arm64 VM series have proven to be a reliable platform for our applications, and we’ve accelerated our plans to deploy Arm64-based Azure solutions.”

A growing solution ecosystem

The Dpsv5 and Epsv5 Azure VM-series feature the Ampere Altra Arm-based processor operating at up to 3.0GHz. The new VMs provide up to 64 vCPUs and include VM sizes with 2GiB, 4GiB, and 8GiB per vCPU memory configurations, up to 40 Gbps networking, and optional high-performance local SSD storage.

The VMs currently in preview support Canonical Ubuntu Linux, CentOS, and Windows 11 Professional and Enterprise Edition on Arm. Support for additional operating systems including Red Hat Enterprise Linux, SUSE Linux Enterprise Server, Debian, AlmaLinux, and Flatcar is on the way.

"We see companies using Arm based architectures as a way of reducing both cost and energy consumption. It's a huge step forward for those looking to develop with Linux on Azure and we are pleased to partner with Microsoft to offer Ubuntu images."—Alexander Gallagher, Vice President of Public Cloud, Canonical

"Red Hat was one of the early leaders in creating common standards around Arm-based platforms, helping to ultimately bring Arm processors to the datacenter and beyond. This aligns with Red Hat’s long-standing commitment to giving our customers a broad set of choices to meet their unique enterprise computing needs, which extends to choice of architecture on-premises and in public clouds. We look forward to supporting Ampere Arm instances on Microsoft Azure as well as continuing our collaboration around the evolution of these platforms with key partners like Microsoft.”—Maryam Zand, Vice President, Cloud Partners, Red Hat

“SUSE has played a significant and active role in the Arm ecosystem, supporting the Arm 64-bit architecture and the Ampere Altra server instances.  SUSE is excited to partner with Microsoft Azure in supporting the Dpsv5 and Epsv5 Azure VM-series based on the Ampere Altra Arm-based server instances in our upcoming SUSE Linux Enterprise Server 15 SP4 release.  Arm-optimized solutions in the cloud offer significant market potential as enterprises improve time to value and scale-out cloud environments with Azure Virtual Machines.  We look forward to continued collaboration with Microsoft Azure.”—Dr. Thomas Di Giacomo, Chief Technology and Product Officer, SUSE

We are also excited about the collaboration with Ampere and Arm. We have been working together to help Azure customers build and manage modern applications at cloud scale.

“Microsoft’s preview of their new Ampere Altra Azure Virtual Machines will provide customers with a first-hand look at its leadership performance across cloud workloads of all types. We have seen rapid growth in the adoption of our Ampere Cloud Native Processors, and this further expands their global scale and availability. Not only do Ampere Altra processors deliver new levels of performance to the cloud, but they are also the efficient and sustainable choice.”—Jeff Wittich, Chief Product Officer Ampere

“Organizations are shifting to a cloud-first approach as modern scale-out workloads diversify, emphasizing the importance of price-performance and power efficiency. The new Microsoft Azure VMs, powered by the Arm Neoverse™-based Ampere Altra platform, highlight our deep collaboration with industry change-makers, and deliver on the power of choice to the cloud computing market.”—Chris Bergey, SVP and GM, Infrastructure Line of Business, Arm.

The next generation of computing technology needs to be designed from the ground up for cloud-native software technologies like microservices, containers, and serverless. To that end, customers will be able to deploy and manage containerized applications with Azure Kubernetes Service (AKS) running on Ampere Altra Arm-based processors.

“As we continue to see customers adopting AKS as their Cloud Native compute platform, providing the price performance of the Ampere Arm-based processor through a consistent managed Kubernetes API gives them the ability to migrate their workloads to drive further efficiencies as they scale up their cloud footprint.”—Sean McKenna, Group Product Manager AKS, Microsoft

Developer platforms and tools

Most major developer platforms and languages are gearing up to, or already provide Arm support and the inherent benefits that this processor architecture brings.

The modern .NET platform introduced native support for the Arm architecture on Linux starting with .NET 5 and has built upon that with the recent .NET 6 release. With C# 10 and F# 6, .NET 6 delivers language improvements that simplify your code. Additionally, a new dynamic profile-guided optimization (PGO) system delivers deep optimizations that are only possible at runtime, driving significant gains in performance that can reduce the cost of running cloud services in Azure, improved cloud diagnostics, and access to many new APIs. With the introduction of native support for Arm in the .NET Framework 4.8.1 (currently in preview and available as part of the latest Windows 11 Insider Preview builds), investments in the vast ecosystem of .NET Framework apps can also now leverage the benefits of running these workloads on Arm.

The latest Microsoft Visual C++ tools (currently in preview and available as part of Visual Studio 17.2 previews) allow you to not just run your apps, but also build natively for Arm, on Arm.

Java has played a critical role in democratizing cross-platform development. With Microsoft's recent JEP 388 contribution to OpenJDK, Java applications can now run on a wider range of Arm systems with no additional changes.

Java developers can enjoy the development experience they are familiar with while building and running their applications with the Microsoft Build of OpenJDK. Microsoft provides binaries for Windows, Linux, and macOS on compatible Arm hardware, for Java 11 and Java 17.

Last, but not least, the totally free Visual Studio Code editor running natively on Arm enables you to harness the power of the cloud for not just your production environment, but now also for your development environment.

General purpose and memory intensive workloads

The new Dpsv5 VM-series are engineered to run several Linux enterprise workloads such as web servers, application servers, open-source databases, .NET applications, Java applications, gaming servers, media servers, and more.

We are also introducing the Dpldsv5 VM-series, which provide 2GiBs per vCPU and offer a combination of vCPUs, memory, and local storage able to cost-effectively run workloads that do not require larger amounts of RAM per vCPU.

Finally, the new Epsv5 VM sizes can meet the requirements associated with memory-intensive Linux-based workloads including open-source databases, in-memory caching applications, gaming, and data analytics engines.

Series	vCPUs	Memory (GiBs)	Local Disk (GiBs)	Max Data Disks	Max NICs
Dpsv5-series	2 – 64	8 – 208	n/a	4 – 32	2 – 8
Dpdsv5-series	2 – 64	8 – 208	75 – 2,400	4 – 32	2 – 8
Dplsv5-series	2 – 64	4 – 128	n/a	4 – 32	2 – 8
Dpldsv5-series	2 – 64	4 – 128	75 – 2,400	4 – 32	2 – 8
Epsv5-series	2 – 32	16 – 208	n/a	4 – 32	2 – 8
Epdsv5-series	2 – 32	16 – 208	75 – 2,400	4 – 32	2 – 8

The Dpsv5, Dplsv5, and Epsv5 VM-series also offer options with no temporary storage at lower price points. You can attach Standard SSDs, Standard HDDs, and Premium SSDs to any of the VMs currently in preview, with Ultra Disk storage support coming soon. Virtual Machine Scale Sets are also supported.

Spot Virtual Machines are available; however, Azure Reserved Virtual Machine Instances pricing will be offered only after the VMs become generally available. Prices vary by region.

Source: microsoft.com

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Azure delivers strong MLPerf inferencing v2.0 results from 1 to 8 GPUs

Microsoft Azure is committed to providing its customers with industry-leading real-world AI capabilities. In December 2021, Microsoft Azure debuted its leadership performance with the MLPerf training v1.1 results. Azure debuted at number one among cloud providers and number two overall at scale among all submitters. Azure’s supercomputer's building blocks were used to generate the results in our v2.0 submissions for the MLPerf inferencing results published on April 6, 2022.

These industry-leading results are driven by Microsoft’s publicly available supercomputing capabilities designed for real-world AI inferencing workloads. Microsoft enables customers of all scales to deploy powerful AI solutions, whether at a focused local scale or at the scale of the largest supercomputers in the world.

Microsoft Azure’s publicly available AI inferencing capabilities are led by the NDm A100 v4, ND A100 v4, and NC A100 v4 virtual machines (VMs) that are powered by NVIDIA A100 SXM and PCIe Tensor Core graphics processing units (GPUs). These results showcase Azure’s commitment to making AI inferencing available to all in the most accessible way—while raising the bar for AI inferencing in Azure.

In our quest to continually provide the best technology for our customers, Azure has recently announced the preview for the NC A100 v4. With this introduction of the NC A100 v4 series, we have provided our customers with three different VM sizes ranging from one to four GPUs. From our benchmarking, we have seen more than two times performance over the previous generation. Azure’s customers can get access to these new systems today by signing up for the preview program.

Some highlights for this round of MLPerf inferencing submissions can be seen in the following tables.

Highlights from the results

ND96amsr A100 v4 powered by NVIDIA A100 80G SXM Tensor Core GPU

Benchmark	Samples/second	Queries/second	Scenarios
bert-99	27,500 plus	~22,500 plus	Offline and server
resnet	300,000 plus	~200,000 plus	Offline and server
3d-unet	24.87		Offline

NC96ads A100 v4 powered by NVIDIA A100 80G PCIe Tensor Core GPU

Benchmark	Samples/second	Queries/second	Scenarios
bert-99	~6,300	~5,300	Offline and server
resnet	144,000	~119,600	Offline and server
3d-unet	11.7		Offline

The above tables showcase three of the six benchmarks the team ran using NVIDIA A100 SXM and PCIe Tensor Core GPUs for offline and server scenarios respectively. Take a look at the full list of results for the various divisions.

Azure works closely with NVIDIA

The results were generated by deploying the environment using the VM offerings and Azure’s Ubuntu 18.04-HPC marketplace image. We worked closely with NVIDIA to quickly deploy the environment and perform benchmarks with industry-leading results in performance and scalability.

These results are a testament to Azure’s focus on offering scalable supercomputing for any workload while enabling our customers to utilize “on-demand” supercomputing capabilities in the cloud to solve their most complex problems.

More about MLPerf

MLPerf is a consortium of AI leaders from academia, research labs, and industry where the mission is to “build fair and useful benchmarks” that provide unbiased evaluations of training and inference performance for hardware, software, and services—all conducted under prescribed conditions. To stay on the cutting edge of industry trends, MLPerf continues to evolve, holding new tests at regular intervals and adding new workloads that represent state-of-the-art AI. MLPerf’s tests are transparent and objective, so users can rely on the results to make informed buying decisions. The industry benchmarking group, formed in May 2018, is backed by dozens of industry leaders. The benchmark tests across inferencing are increasingly becoming the key tests that hardware and software vendors use to demonstrate performance. Take a look at the full list of results for MLPerf Inference v2.0.

Source: microsoft.com

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The future is on FHIR for SAS and Microsoft Azure

This blog is part of a series in collaboration with our partners and customers leveraging the newly announced Azure Health Data Services. Azure Health Data Services, a platform as a service (PaaS) offering designed to support Protected Health Information (PHI) in the cloud, is a new way of working with unified data—providing care teams with a platform to support both transactional and analytical workloads from the same data store and enabling cloud computing to transform how we develop and deliver AI across the healthcare ecosystem.

There is a dichotomy in health care technology. Despite new developments in imaging, diagnostics, treatment, and surgical techniques, the lack of data standardization in the industry has trapped health insights in functional silos. Providers and payers alike struggle to manually reconcile incompatible file formats, which slows the transfer of information and negatively impacts quality care and patient experience.

Microsoft, along with partners such as global analytics software company SAS, are driving towards increased interoperability through enabling the use of standards such as Fast Healthcare Interoperability Resources (FHIR®). Together, SAS and Microsoft Azure are building deep technology integrations that unlock value by making disparate data and advanced analytics more accessible to health and life science organizations. With new capabilities such as the integration from Azure Health Data Services to SAS on Azure, the embedded AI capabilities of SAS Health are more efficient and secure, expanding the possibilities of patient-centric innovation and trusted collaboration across the health landscape.

FHIR puts the patient at the center of the health care ecosystem. When querying information in the previous HL7 format, the query is answered with the entire patient dataset that must be parsed to find the information desired for predictive modeling. Additionally, data would require harmonization within and across the organization, creating limitations on available data. In contrast, harmonized FHIR datasets persisting on Azure Health Data Services enable FHIR-based requests directed to the specific data points required, speeding up queries to near-real-time and protecting patient data.

While FHIR’s footprint in the industry is small compared to HL7’s, the global adoption of the FHIR standard is growing. Major electronic health records (EHR) companies like Cerner and Epic are moving quickly to support FHIR. Notably in the United States, the Centers for Medicare and Medicaid Services (CMS) has mandated its use for health insurance payers and providers.

Transform your analytical experience in the health cloud

The integration between Azure Health Data Services and SAS Health can be transformational for organizations who have struggled to operationalize analytics. Not only does this integration offer a technology that is secure, fast, and scalable, it democratizes analytics by allowing the business or clinical user to query a patient data set using a pre-set parameter or algorithm and return results within a clinical workflow.

The traditional view of health analytics is that it occurs outside the process of care and is in some way removed from the patient. That’s changing, thanks to secure health cloud environments like Azure Health Data Services and presents the opportunity for more real-time integration of patient and claims data. With the evolution of the citizen data scientist and respective interoperability, we now see a clearer path from analytics to improved health care outcomes.

The graphic below illustrates the role of health data analytic interoperability in health and life sciences. Ultimately, the

SAS Health and Azure Health Data Services

The embedded-AI capabilities of SAS Health running on FHIR data ingested through Azure Health Data Services provide game-changing advantages across health care delivery and research.

Providers

SAS Health on FHIR gives speedy access to analytic insights within EHRs, parsing out only the information needed, allowing near-real-time results from, for example, pharmacy claims, laboratory results, or imaging. Predictive insights such as medication adherence or emerging health risks are more available through a secure FHIR-based exchange. Quality care and patient satisfaction increase when providers can integrate data across multiple systems and record types including patient records and claims data into a single view.

Payers

Payers governed by CMS are already mandated to transition to FHIR-based communication standards and are experiencing early wins. For example, adjudication of claims is one of the most time-consuming parts of the payer process. With FHIR, payers can securely query patient records to determine medical necessity of a service or procedure and whether appropriate authorization was obtained, cutting time dramatically in the process. With FHIR’s extensibility beyond the payer-provider core, pharmacy data can be queried to inform proactive disease management programs with specialty drugs and more real-time formulary approvals to meet patient needs.

Academic researchers

For clinical research, data sharing can be a common, time-consuming obstacle. FHIR-ready datasets can accelerate the generation of new health insights and expand the universe of data types for research, including social determinants of health, real-world data, genetics, device data from the internet of medical things, and more.

Ultimately, these innovations in health data analytic interoperability can make insights faster across the vast ecosystem of professionals who are committed to a healthier world. While technology is only one part of the solution, improving health begins with predicting future health risks and taking proactive steps to mitigate disease and promote physical and mental wellness.

Do more with your data with Microsoft Cloud for Healthcare

With Azure Health Data Services, health organizations can transform their patient experience, discover new insights with the power of machine learning and AI, and manage PHI data with confidence. Enable your data for the future of healthcare innovation with Microsoft Cloud for Healthcare.

Source: microsoft.com

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Empowering space development off the planet with Azure

Any developer can be a space developer with Azure. Microsoft has a long history of empowering the software development community. We have the world’s most comprehensive developer tools and platforms from Github to Visual Studio, and we support a wide range of industries and use cases from healthcare, financial services, critical industries, and now space.

Read More: Microsoft Certifications

As Microsoft expands its focus toward space, we are bringing the power, approachability, and security of our developer story to the next frontier. Microsoft is empowering developers with a platform for on-orbit compute at the ultimate edge, so that spacecraft running AI workloads are connected to the hyperscale Azure cloud.

We are reducing the barriers to entry for space application development and increasing the flexibility and modularity of software solutions. Enabling those building space workloads to easily leverage the productivity of our developer tools and integration with Azure services—to develop, analyze, deploy, and operate space applications in orbit and on the ground.

Today we are bringing new partnerships and capabilities to the development community, including:

◉ NASA and Hewlett Packard Enterprise (HPE) are testing AI at the ultimate edge for Astronaut Safety.

◉ New partnerships are bringing development capabilities to on-orbit compute.

   ◉ Unlocking new on-orbit climate data applications with Thales Alenia Space (TAS).

   ◉ Developing new technologies with Loft Orbital to demonstrate re-taskable satellite functions and seamless connectivity to the terrestrial cloud.

   ◉ Demonstrating reconfigurable on-orbit compute and AI processing with Ball Aerospace.

◉ Rapidly analyzing spaceborne data with the new reference architecture for Azure Orbital with Azure Synapse.

◉ Empowering analysts with newly integrated Blackshark.ai geospatial models are available with Azure Orbital.

Testing AI for Astronaut Safety at the ultimate edge

Microsoft, NASA, and HPE developed an AI workload test to run on the International Space Station (ISS) that could detect damage to astronaut equipment.

Using Microsoft’s cloud computing platform, NASA and Microsoft created a computer vision application that identifies the condition of the space gloves. Once trained in the cloud, the app was deployed to the HPE Spaceborne Computer-2, an AI-enabled software and hardware platform, aboard the ISS, and then operated at the ultimate edge enabling both local and remote analysis of the glove conditions.

On-orbit partnerships

Thales Alenia Space unlocks new on-orbit climate data applications with Microsoft to gather unmatched Earth observation insights.

Microsoft is partnering with Thales Alenia Space to demonstrate and validate on-orbit compute technologies with a demonstration onboard the International Space Station (ISS). Thales Alenia Space, a joint venture between Thales (67 percent) and Leonardo (33 percent), is the leader in orbital infrastructures and is developing high-power, edge-computing solutions for space.  Microsoft and Thales Alenia Space will deploy a powerful on-orbit computer, an on-orbit application framework, and high-performance Earth Observation sensors to unlock new on-orbit climate data processing applications for the benefit of our planet's sustainability. In collaboration with Microsoft Research (MSR), Microsoft and Thales Alenia Space will work with research teams in remote sensing, computer vision, and climate science to demonstrate the potential of next-generation on-orbit compute for Earth observation. This space edge computing capacity will allow gathering faster, to-the-point Earth observation insights immediately applicable for our planet’s surveillance, understanding, and protection. This joint collaboration comes a year after the integration of Deeper Vision, an Earth observation data analytics software by Thales Alenia Space, into Azure Space and is a strong milestone towards joint strategic ambitions between Microsoft and Thales Alenia Space which have just signed a Memorandum of Understanding on geospatial solutions, digital ground segment, and space edge computing.

New partnership with Loft Orbital to advance space edge computing and software deployment to orbit.

The Microsoft and Loft Orbital partnership will enable a new way to develop, test, and validate software applications for space systems in Microsoft Azure, and then seamlessly deploy them to satellites in orbit using Loft's space infrastructure tools and platforms. This solution also offers more efficient paths to flight for modern ‘massless’ payloads, where parties needing space capabilities can leverage shared on-orbit hardware rather than having to build and launch their own.

Working together with Loft, we are bringing core satellite capabilities like tasking which has typically been executed on the ground, to a more agile commanding and tasking paradigm executed on-orbit. To do so, we are integrating the Microsoft Azure suite of products, including terrestrial cloud and ground stations services, with Loft software capabilities that provide access to spacecraft, including on-orbit edge computing environment and sensors.

This strategic partnership will provide government and commercial users with a scalable and simplified capability to deploy software in space, enabling new paradigms in remote sensing, edge compute, on-orbit autonomy, and other areas. This groundbreaking capability will be brought to market first on a jointly used satellite launching in 2023 that will provide a host environment for third-party software applications, enabling users to deploy and operate their applications in orbit.

Demonstrating reconfigurable on-orbit compute processing with Ball Aerospace

Ball Aerospace, a systems integrator with a heritage of designing and building government satellite programs and mission applications, is planning a series of on-orbit testbed satellites that target the agile implementation of new software and hardware for the US Government. Together, Ball Aerospace and Microsoft are collaborating on the execution of these spacecraft missions to demonstrate reconfigurable on-orbit processing technologies, leveraging the Azure Cloud. This includes the use of containerization and cloud on the edge to enable a software-defined mission approach that embraces standards such as Sensor Open Systems Architecture (SOSA), Universal Command and Control Interface (UCI), and Open Mission Systems (OMS). Modular and reconfigurable on-orbit compute will support multiple complex missions for the United States Government and grant the ability to support future concepts for smaller, agile, multi-mission capabilities across all federal space programs.

Analytics for spaceborne data using Azure Orbital

Satellite imagery is a valuable asset; using AI with satellite imagery is a value multiplier. Using geospatial AI over the same area of interest with regularly refreshed satellite imagery, analysts can monitor change detection for their respective areas of interest.

The use of AI with satellite imagery is a powerful, cost-effective tool spanning all industries that monitor, measure, and/or monetize large areas of the Earth. Extracting this value is hard work as satellite imagery consists of unstructured, big data that requires significant resources to transform and analyze in order to access information and store and use it as structured data.

The Azure Space team released a reference architecture articulating how to apply AI to satellite imagery at scale using Azure resources. This reference architecture makes use of Azure Synapse Analytics, Azure Data Lake Store Gen 2, Apache Spark Pool, Azure Data Share, Azure Batch, and Azure Container Registry. This Azure workflow reduces the complexity of extracting insights from remote sensing data by articulating how to group Azure resources to ingest, store, transform, and apply AI over satellite imagery then use the results for various applications. Azure resources allow for flexibility in the workflow, management of storage options, parallelization of workload, and (re)use of containerized models.

Given Azure’s orchestration flexibility, customers can bring their own imagery. Alternatively, if a customer needs imagery, they can call another imagery provider API specifying the respective area of interest, resolution, and vintage of their choosing through Microsoft’s partner Airbus Defense and Space or Microsoft’s Planetary Computer as an option. Customers can also bring their own trained models into the orchestration. If a customer needs geospatial intelligence and remote sensing AI, Microsoft has partnerships with Blackshark.ai, Orbital Insight, and Esri. For those customers looking to build AI, Microsoft offers tools like Azure Machine Learning and Azure Custom Vision.

Blackshark.ai geospatial models are available for analytics on Azure

Blackshark.ai is offering an end-to-end geospatial platform. Part of this platform is the geospatial analytics service called Orca, which detects objects, and extracts attributes about buildings, vegetation, and a growing number of other detection classes, such as roads or infrastructure in the future. This service is now available through Azure Synapse Analytics.

The containerized Orca service–fully integrated into Azure Synapse Analytics provides fast, global-scale, and accurate insights based on satellite or aerial imagery data sets that are available via Azure or provided by customers. Whenever fresh input data is available, the Orca service can provide precise insights for object and change detection, enabling applications such as efficient 3D mapping services, logistic planning, risk analysis, telecom signal propagation planning, or disaster relief planning.

Source: microsoft.com

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Introducing the new Azure Front Door: Reimagined for modern apps and content

In 2019, we launched Azure Front Door to bring enterprise-grade content delivery network (CDN) capabilities to our customers. This was a result of our own cloud journey over the past 13 years, which led us to develop unique infrastructure and services hardened by support for Microsoft’s largest applications like Bing, Microsoft 365, LinkedIn, Skype, and Azure. Read about LinkedIn’s experience1 migrating their own infrastructure to Azure Front Door.

Since then, a lot has changed for you and your customers. The acceleration of digital transformation to adapt to new ways of doing business, hybrid working models, and increasing security costs has driven the demand for a new type of cloud CDN that can address these modern challenges and simplify internet-first architectures in the cloud.

Today, we are announcing the general availability of the new Azure Front Door, our native, modern cloud content delivery network (CDN) catering to both dynamic and static content acceleration with built-in turnkey security, and a simple and predictable pricing model. There are two Azure Front Door tiers—Azure Front Door Standard and Premium—that provide a unified, secure solution for delivering your applications, APIs, and content on Azure or anywhere.

Azure Front Door: The modern enterprise CDN

Every company is now a technology company challenged with managing a rapidly growing digital footprint, dispersed workforce, and evolving security threats. As a result, enterprises are looking for solutions that help meet the rising demands for better scalability, more security, higher performance, greater automation, and easier manageability—with reduced costs.

Whether you’re delivering content and files or building global apps and APIs, Azure Front Door can help you deliver higher availability, lower latency, better scale, and more secure experiences to your users wherever they are. Azure Front Door also enables you to define, manage, and monitor the global routing for your app.

Dynamic and static content acceleration with intelligent security

With the addition of Azure Front Door Standard and Premium, two new tiers that combine the capabilities of Azure Front Door (classic) and Azure CDN from Microsoft (classic) and attach with Azure Web Application Firewall (WAF), Azure Front Door is now a unified, modern cloud CDN platform with intelligent threat protection and simple to understand pricing model, built on Microsoft’s massive-scale private global network.

Azure Front Door now also provides a rich set of advanced capabilities that enhance the DevOps experience, security posture, and cost-effectiveness for enterprise customers migrating and/or deploying high-performance, scalable, and secure applications on Azure or anywhere.

The key benefits you can get from Azure Front Door include:

Modern architecture

Build and operate dynamic, high-quality digital experiences with highly automated, secure, and reliable platforms.

◉ Deeply integrated experiences with other Azure applications and services such as domain name system (DNS) and Web Apps to improve manageability and speed up deployment. We now offer DNS TXT record-based validation to simplify and reduce delays around custom domain validation.

◉ Improved automation and simplified provisioning with cloud-native and DevOps friendly command line tools. For example, you can now provision custom domains along with other resources in one deployment and validate the domain ownership afterward or use the new Quick Create option in the portal to dramatically reduce deployment and configuration steps.

◉ Enhanced analytics capabilities such as access logs, health probe logs, additional metrics, and pre-built traffic and security reports for more effective monitoring, troubleshooting, and debugging.

◉ Expanded rules at the edge with enhanced rules engine capabilities adding regular expressions and server variables that let you move more of your business logic to the edge and create more complex and dynamic routing between your users and backends.

Fast global delivery

Deploy your apps and content at scale to your users wherever they are—creating opportunities for you to compete, weather change, and quickly adapt to new demand and markets. 

◉ A truly global network built by Microsoft with hundreds of edge locations connected to Azure via a private WAN that can improve latency for apps by up to three times and provides enterprise-grade reliability and massive scalability to deliver low latency and high throughput for consistent application experiences.

◉ Unified static and dynamic delivery is offered in a single service to accelerate and scale your application and with real-time routing to develop high-availability experiences for applications hosted in Azure or anywhere.

◉ A simplified cost model that reduces billing complexity by having fewer meters customers need to plan for and integrated egress (data transfer outbound) pricing that removes the separate egress charge from Azure regions to Azure Front Door.

Intelligent security

Protect your digital estate against known and new threats with intelligent security that embrace a Zero Trust framework.

◉ Best-of-breed security services seamlessly attached such as built-in layer 3-4 DDoS protection, Web Application Firewall, Azure DNS to protect your domains, and Azure Private Link.

◉ WAF enhancements offer a powerful, yet cost-effective protection from common attacks and bots and are customizable to application-specific detections. Azure Front Door Premium includes Azure Web Application Firewall at no additional cost and provides enhanced capabilities. Azure WAF is also releasing a new DRS 2.0 RuleSet, offering reduced false positives and anomaly scoring-based detection. Bot manager—now generally available, provides an additional layer of Bot detection based on Microsoft Threat Intelligence.

◉ Azure Private Link support on Azure Front Door Premium with availability in all Azure regions with availability zones, enabling your application to extend all the way out to the edge with private access from Azure Front Door to your backends in Azure.

Azure Front Door (classic) and Azure CDN from Microsoft (classic)

The existing Azure Front Door and Azure CDN from Microsoft will now be known as Azure Front Door (classic) and Azure CDN from Microsoft (classic) moving forward. Azure Front Door (classic), as well as Azure CDN from Microsoft (classic), will continue to be fully supported and you can continue to use them. However, we encourage you to take advantage of Azure Front Door Standard and Premium as the latest capabilities and future enhancements will not be available on Azure Front Door (classic).

Over the coming months, we will be launching zero downtime migrations from Azure Front Door (classic) and Azure CDN from Microsoft (classic) to Azure Front Door Standard and Premium. Please stay tuned for more updates. If you are new to Azure Front Door, you can easily launch Azure Front Door Standard and Premium in the Azure portal or using our API.

Source: microsoft.com

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Bring your own IP addresses (BYOIP) to Azure with Custom IP Prefix

When planning a potential migration of on-premises infrastructure to Azure, you may want to retain your existing public IP addresses due to your customers' dependencies (for example, firewalls or other IP hardcoding) or to preserve an established IP reputation. Today, we are excited to announce the general availability of the ability to bring your own IP addresses (BYOIP) to Azure in all public regions. Using the Custom IP Prefix resource, you can now bring your own public IPv4 ranges to Azure and use them like any other Azure-owned public IP ranges. Once onboarded, these IPs can be associated with Azure resources, interact with private IPs and VNETs within Azure’s network, and reach external destinations by egressing from Microsoft’s Wide Area Network. Read more about how bringing your IP addresses to Azure can help to speed up your cloud migration.

Provisioning a custom IP range

Onboarding your ranges to Azure can be done through the Azure portal, Azure PowerShell, Azure CLI, or by using Azure Resource Manager (ARM) templates. In order to bring a public IP range to use on Azure, you must own and have registered the range with a Routing Internet Registry such as ARIN or RIPE. When bringing an IP range to use on Azure, it remains under your ownership, but Microsoft is permitted to advertise it from our Wide Area Network (WAN). The ranges used for onboarding must be no smaller than a /24 (256 IP addresses) so that they will be accepted by Internet service providers. When you create a Custom IP Prefix resource for your IP range, Microsoft performs validation steps to verify your ownership of the range and its association with your Azure subscription. Each onboarded range is associated with an Azure region.

Using a custom IP range

Once your range has been provisioned on Azure, you have the option to assign public IP addresses from the range to resources immediately or to begin advertising the range before assigning, depending on what fits your specific use case. After the command is issued to commission a range, Microsoft will advertise it both regionally (within Azure) and globally (to the Internet). The specific region where the range was onboarded will also be posted publicly for geolocation providers. To assign the BYOIPs, you would create public IP prefixes (contiguous blocks of Standard SKU public IP addresses), from which you can allocate specific individual public IP addresses. Note that while an IP range is onboarded under the context of an Azure subscription, prefixes from this range can be derived from other subscriptions with appropriate permissions. Onboarded IPs can be associated with any resource that supports Standard SKU public IPs, such as virtual machines, Standard Public Load Balancers, Azure Firewalls, and more. You are not charged for maintenance and hosting of your onboarded Public IPs Prefix; you are charged only for egress bandwidth from the IPs and any attached resources.

Key takeaways

◉ The ability to bring your own IP addresses (BYOIP) to Azure is currently available in all regions.

◉ The minimum size of an onboarded range is /24 (256 IP addresses).

◉ Onboarded IPs are put in a Custom IP Prefix resource for management, from which Public IP Prefixes can be derived and utilized across subscriptions.

◉ You are not charged for the hosting or management of onboarded ranges brought to Azure.

Source: microsoft.com

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