da Agency

Microsoft Azure's defense in depth approach to cloud vulnerabilities

Our digital world is changing, with more persistent, sophisticated, and driven cybercriminals. As risks increase and threats compound, trust is more important than ever. Customers need to be able to trust in the technology platforms they invest in to build and run their organizations. As one of the largest cloud service providers, we build trust by helping our customers be secure from the start and do more with the security of our cloud platforms that’s built in, embedded, and out of the box.

Our security approach focuses on defense in depth, with layers of protection built throughout all phases of design, development, and deployment of our platforms and technologies. We also focus on transparency, making sure customers are aware of how we’re constantly working to learn and improve our offerings to help mitigate the cyberthreats of today and prepare for the cyberthreats of tomorrow.

In this blog, we highlight the extensive security commitments from our past, present, and into the future, as well as where we see opportunities for continued learning and growth. This piece kicks off a 4-part Azure Built-In Security series intended to share lessons we’ve learned from recent cloud vulnerabilities and how we're applying these learnings to ensure our technologies and processes are secure for customers. Transparently sharing our learnings and changes is part of our commitment to building trust with our customers, and we hope it encourages other cloud providers to do the same.

Past, present, and future of our security commitments 

For decades Microsoft has been, and continues to be, deeply focused on customer security and improving the security of our platforms. This commitment is evident in our long history of leading security best practices from our on-premises and software days to today’s cloud-first environments. A shining example of this is when in 2004, we pioneered the Security Development Lifecycle (SDL), a framework for how to build security into applications and services from the ground up whose influence has been far reaching. SDL is currently used as the basis for built-in security in key initiatives including international application security standrards (ISO/IEC 27034-1) and the White House’s Executive Order on Cyber Security.

As security leaders and practitioners know though, security’s job is never done. Constant vigilance is vital. This is why Microsoft currently invests heavily in internal security research as well as a comprehensive bug bounty program. Internally, Microsoft boasts more than 8,500 security experts constantly focused on vulnerability discovery, understanding attack trends and addressing patterns of security issues. Our world-class security research and threat intelligence helps protect customers, Microsoft, open-source software, and our industry partners alike.

We also invest in one of the industry’s most proactive Bug Bounty Programs. In 2021 alone, Microsoft awarded $13.7 million in bug bounties across a broad range of technologies. An emerging trend over the last year has been an uptick in externally reported vulnerabilities impacting several cloud providers, including Azure. While vulnerabilities are not uncommon across the industry, as a leading cloud provider and the number one security vendor, Microsoft is of greater interest to researchers and security competitors alike. This is why our public bounty program was the first to include cloud services, beginning in 2014, and in 2021 we further expanded the program to include higher rewards for cross-tenant bug reports. As anticipated, this clearly drew even more external security researcher interest in Azure, culminating in multiple cross-tenant bug bounties being awarded. Regardless of the reasons, these findings helped further secure specific Azure services and our customers.

Finally, we firmly believe that security is a team sport, and our focus on collaboration is evidenced in our contributions to the security ecosystem, such as our involvement in the NIST Secure Software Development Framework (SSDF), and improving the security posture of Open Source Software (OSS) through our $5 million investment in the OpenSSF Alpha-Omega project.

Our commitment to security is unwavering, as seen in our decades-long leadership of SDL to present day vulnerability discovery, bug bounty programs, collaboration contributions, and continues well into the future with our commitment of investing more than $20 billion over five years in cybersecurity. While building-in security from the start is not new at Microsoft, we understand the security landscape is continually changing and evolving, and with it so should our learnings.

Our latest learnings and improvements for a more secure cloud

At Microsoft, a core part of our culture is a growth mindset. Findings from internal and external security researchers are critical to our ability to further secure all our platforms and products. For each report of a vulnerability in Azure, we perform in-depth root cause analysis and post-incident reviews whether discovered internally or externally. These reviews help us reflect and apply lessons learned, at all levels of the organization, and are paramount to ensuring that we constantly evolve and build in security at Microsoft.

Based on the insights we’ve gained from recent Azure vulnerability reports, we are improving in three key dimensions. These developments enhance our response process, extend our internal security research, and continually improve how we secure multitenant services.

1. Integrated response

Several lessons from the past year focused our attention in areas we recognize the need to improve, such as accelerating response timelines. We are addressing this throughout our Integrated Response processes and unifying internal and external response mechanisms. We started by increasing both the frequency and scope of our Security LiveSite Reviews at the executive level and below. We are also improving the integration of our external security case management and our internal incident communication and management systems. These changes reduce mean time to engagement and remediation of reported vulnerabilities, further refining our rapid response. 

2. Cloud Variant Hunting

In response to cloud security trends, we have expanded our variant hunting program to include a global and dedicated Cloud Variant Hunting function. Variant hunting identifies additional and similar vulnerabilities in the impacted service, as well as identify similar vulnerabilities across other services, to ensure discovery and remediation is more thorough. This also leads to a deeper understanding of vulnerability patterns and subsequently drives holistic mitigations and fixes. Below are a few highlights from our Cloud Variant Hunting efforts:

In Azure Automation we identified variants and fixed more than two dozen unique issues.
In Azure Data Factory/Synapse we identified significant design improvements that further harden the service and address variants. We also worked with our supplier, and other cloud providers, to ensure that risks were addressed more broadly.
In Azure Open Management Infrastructure we identified multiple variants, our researchers published CVE-2022-29149, and we drove the creation of Automatic Extension Upgrade capabilities to reduce time to remediate for customers. Our Automatic Extension Upgrade feature is already benefiting Azure Log Analytics, Azure Diagnostics, and Azure Desired State Configuration customers.

Additionally, Cloud Variant Hunting proactively identifies and fixes potential issues across all our services. This includes many known as well as novel classes of vulnerabilities, and in the coming months we will share more details of our research to benefit our customers and the community at large

3. Secure multitenancy

Based on learnings from all our security intelligence sources, we continue to evolve our Secure Multitenancy requirements as well as the automation we use at Microsoft to provide early detection and remediation of potential security risk. As we analyzed Azure and other cloud security cases over the last couple of years, both our internal and external security researchers have found unique ways to break through some isolation barriers. Microsoft invests heavily in proactive security measures to prevent this, so these new findings helped determine the most common causes and ensure we were committed to addressing them within Azure through a small number of highly leveraged changes.

We are also doubling down on our defense in depth approach by requiring and applying even more stringent standards for Compute, Network, and Credential isolation across all Azure services, especially when consuming third-party or OSS components. We are continuing to collaborate with the OSS community, such as PostgreSQL, as well as other cloud providers, on features which are highly desirable in multitenant cloud environments. 

This work has already resulted in dozens of distinct findings and fixes with the majority (86 percent) attributed to our specific improvements in Compute, Network, or Credential isolation. Among our automation improvements, we are extending internal Dynamic Application Security Tests (DAST) to include more checks for validating Compute and Network isolation as well as adding net new runtime Credential isolation check capabilities. In parallel, our security experts continue to scrutinize our cloud services, validate they meet our standards, and innovate new automated controls for the benefit of our customers and Microsoft.

From the cloud security’s shared responsibility model, we recommend our customers use the Microsoft cloud security benchmark to improve their cloud security posture. We are developing a set of new recommendations focusing on multi-tenancy security best practices and will publish that in our next release.

In short, while Microsoft has a long and continued commitment to security, we are continually growing and evolving our learnings as the security landscape also evolves and shifts. In this spirit of constant learning, Microsoft is addressing recent Azure cloud security issues by enhancing secure multitenancy standards, expanding our cloud variant hunting capacity, and developing integrated response mechanisms. Our enhancements, and the scale of our security efforts, further demonstrate our leadership and decades-long commitment to continual improvement of our security programs and raising the bar for security industry-wide. We continue to be committed to integrating security into every phase of design, development, and operations so that our customers, and the world, can build on our cloud with confidence.

Learn more

Follow the Microsoft Security Response Center blog for our latest security research findings.
Learn more about how Microsoft Azure can help strengthen your security posture.
To learn more about our responses to cloud security updates, read our blogs: the Anatomy of a Cloud-Service Security Update and Anatomy of a Security Update.

Quelle: Azure

da Agency

Microsoft and Isovalent partner to bring next generation eBPF dataplane for cloud-native applications in Azure

This post was co-authored by Narayan Annamalai, Partner PM Manager, Microsoft Azure.

As cloud-native applications are experiencing astronomical growth, customers are constantly demanding to scale their Kubernetes and containerized deployments with rich feature sets for network security and observability and without degrading their network performance. With the above in mind, we are excited about this partnership to enhance our platform to bring the power of eBPF natively in Microsoft Azure that will efficiently meet networking capabilities like container network security, cluster service routing, and network observability at a large scale while maintaining mission-critical uptime and reliability.

Microsoft and Isovalent bring new features with eBPF to Azure Kubernetes Services

Microsoft announces the strategic partnership with Isovalent to bring Cilium's eBPF-powered networking data plane and enhanced features for Kubernetes and cloud-native infrastructure.

Azure Kubernetes Services (AKS) will now be deployed with Cilium open-source data plane and natively integrated with Azure Container Networking Interface (CNI).

Microsoft and Isovalent will enable Isovalent Cilium Enterprise as a Kubernetes container App offering onto Azure Container Marketplace. This will provide a one-click deployment solution to Azure Kubernetes clusters with Isovalent Cilium Enterprise advanced features.

Azure CNI powered by Cilium

Recently, Microsoft announced the preview of Azure CNI powered by Cilium for Azure Kubernetes Services.

Cilium’s expertise in eBPF-enriched features like efficient load-balancing, extensive network security features, and rich monitoring along with industry-leading robust and scalable Azure CNI IP Address Management (IPAM), VNET and Overlay mode, will give the most performant and best-in-class container networking platform for our AKS customers.

As a native offering, customers will find it significantly easier to leverage Cilium open-source features directly on AKS Cluster creations as opposed to multi-step configuration via bring your own CNI (BYOCNI) or custom configurations.

Microsoft will handle first-line support and collaborate with Isovalent on specific support issues to their deep knowledge of the technology. We are thrilled to be expanding our relationship with Isovalent and continuing our collaboration with the Cilium open-source community. Together, we have built an ideal platform for current benefits and future innovations.

Isovalent Cilium Enterprise in Azure Container Marketplace

Microsoft and Isovalent are actively collaborating to bring the Isovalent Cilium Enterprise offering onto Azure Kubernetes Services via Azure Container Marketplace.

With this tight integration, customers who also want advanced Isovalent Cilium Enterprise capabilities, will be offered one-click deployment and upgrade for Cilium Enterprise features to a new or existing AKS cluster with a much simpler and more reliable experience. Cilium Enterprise will be built with native integration with Azure networking platform to offer advanced features and capabilities with best-in-class performance and scale. Microsoft and Isovalent will also collaborate to include joint testing, compatibility, and versioning checks, along with seamline support to ensure Cilium Enterprise runs best on Azure.

Customers will also get a unified billing experience, auto-upgrades of Enterprise version updates, usage, and all the other key features offered by the Azure Marketplace platform. This will eliminate a lot of management overhead, which otherwise customers would have to manage manually.

The preview for this new offering will be available early next year.

Learn more

Find out more about this strategic partnership from Thomas Graf, Co-founder, and CTO of Isovalent.

Request early access

By submitting, you (1) agree to share your information with Microsoft and Isovalent, (2) agree to Microsoft and Isovalent's privacy policy, and (3) agree to receive occasional emails about our products and services. You may unsubscribe from these communications at any time.
Quelle: Azure

da Agency

Improve speech-to-text accuracy with Azure Custom Speech

With Microsoft Azure Cognitive Services for Speech, customers can build voice-enabled apps confidently and quickly in more than 140 languages. We make it easy for customers to transcribe speech to text (STT) with high accuracy, produce natural-sounding text-to-speech (TTS) voices, and translate spoken audio. In the past few years, we are inspired by the ways customers seek our customization features to fine-tune speech recognition to their use cases.

As our speech technology continues to change and evolve, we want to introduce four custom speech-to-text capabilities and their respective customer use cases. With these features, you can evaluate and improve the speech-to-text accuracy for your applications and products. A custom speech model is trained on top of a base model. With a custom model, you can improve recognition of domain-specific vocabulary by providing text data to train the model. You can also improve recognition based on the specific audio conditions of the application by providing audio data with reference transcriptions.

Custom Speech data types and use cases

Our Custom Speech features will let you customize Microsoft's speech-to-text engine. You will be able to customize the language model by tailoring it to the vocabulary of the application and customize the acoustic model to adapt to the speaking style of your users. By uploading text and/or audio data through Custom Speech, you'll be able to create these custom models, combine them with Microsoft's state-of-the-art speech models, and deploy them to a custom speech-to-text endpoint that can be accessed from any device.

Phrase list: A real-time accuracy enhancement feature that does not need model training. For example, in a meeting or podcast scenario, you can add a list of participant names, products, and uncommon jargon using phrase list to boost their recognition.

Plain text: Our simplest custom speech model can be made using just text data. Customers in the media industry use this in use cases such as commentary of sports events. Because each sporting event’s vocabulary differs significantly from others, building a custom model specific to a sport increases accuracy by biasing to the vocabulary of the event.

Structured text: This is text data that boosts patterns of sentences in speech. These patterns could be utterances that differ only by individual words or phrases, for example, “May I speak with name” where name is a list of possible names of individuals. The pattern can link to this list of entities (name in this case), and you can also provide their unique pronunciations.

Audio: You can train a custom speech model using audio data, with or without human-labeled transcripts. With human-labeled transcripts, you can improve recognition accuracy on speaking styles, accents, or specific background noises. For American English, you can now train without needing a labeled transcript to improve acoustic aspects such as slight accents, speaking styles, and background noises.

Research milestones

Microsoft’s speech and dialog research group achieved a milestone in reaching human parity in 2016 on the Switchboard conversational speech recognition task, meaning we had created technology that recognized words in a conversation as well as professional human transcribers. After further experimentation, we then followed up with a 5.1 percent word error rate, exceeding human parity in 2017. A technical report published outlines the details of our system. Today, Custom Speech helps enterprises and developers improve upon the milestones achieved by Microsoft Research.

Customer inspiration

Peloton: In the past, Peloton provided subtitles only for its on-demand classes. But that meant that the signature live experience so valued by members was not accessible to those who are deaf or hard of hearing. While the decision to introduce live subtitles was clear, executing on that vision proved a bit murkier. A primary challenge was determining how automated speech recognition software could facilitate Peloton’s specific vocabulary, including the numerical phrases used for class countdowns and to set resistance and cadence levels. Latency was another issue—subtitles wouldn’t be very useful, after all, if they lagged behind what instructors were saying. Peloton chose Azure Cognitive Services because it was cost-effective and allowed Peloton to customize its own machine learning model for converting speech to text—and was significantly faster than other solutions on the market. Microsoft also provided a team of engineers that worked alongside Peloton throughout the development process.

Speech Services and Responsible AI

We are excited about the future of Azure Speech with human-like, diverse, and delightful quality under the high-level architecture of the XYZ-code AI framework. Our technology advancements are also guided by Microsoft’s Responsible AI process, and our principles of fairness, inclusiveness, reliability and safety, transparency, privacy and security, and accountability. We put these ethical standards into practice through the Office of Responsible AI (ORA)—which sets our rules and governance processes, the AI Ethics and Effects in Engineering and Research (Aether) Committee—which advises our leadership on the challenges and opportunities presented by AI innovations, and Responsible AI Strategy in Engineering (RAISE)—a team that enables the implementation of Microsoft Responsible AI rules across engineering groups.

Get started with Azure Cognitive Services for Speech

You can use Speech Studio to test how custom speech features would help improve recognition for your audio. In addition, start building new customer experiences with Azure Neural TTS and STT. In addition, the Custom Neural Voice capability enables organizations to create a unique brand voice in multiple languages and styles.

Resources

Try out Speech services in the Studio.
Get started with Custom Speech.
Get started with speech to text.
Get started with text to speech.
Get started with Custom Neural Voice.
Get started with speech translation.

Quelle: Azure

da Agency

Voltus and Azure—no power integrity challenge too big to solve

This post was co-authored by Giancarlo DiPasquale, Microsoft Director, Semiconductor & EDA; Rajat Chaudhry, Product Management Director, Cadence; and Adrian Lao, Senior Software Architect, Cadence.

With the advent of AI and hyperscale designs on advanced nodes, it is common to see designs in over 50 billion transistor categories with tens to 100 billion plus nodes in the on-chip power network. This explosion in scale requires solutions that meet the following requirements:

High performance and capacity.
Elasticity.
Manage varying compute resource requirements.
Low cost to manage the exponential increase in compute requirements.

Voltus on Azure

Voltus is a leading IC Power Integrity Signoff Solution from Cadence Design Systems. It is used by top chip design companies to verify the reliability of their power networks on chip (NoC) and enables power integrity and thermal analysis at the system level.

Microsoft Azure provides a cloud-based high-performance computing (HPC) infrastructure with security, reliability, and scalability that is a natural fit for electronic design automation (EDA) workloads, especially power integrity analysis.

Azure can support both a hybrid model as well as an all-in model. In the hybrid model customers mainly use their on-premises infrastructure but can add to their compute and storage capacity on an on-demand basis to satisfy peak demand. The hybrid approach is typically used by customers new to using the cloud. In an all-in model, customers primarily use Azure infrastructure for all their EDA workloads. The all-in model is a great use case for startups and customers who really want to optimize their costs while taking advantage of the scale and flexibility of Azure. Voltus supports both the hybrid as well as the all-in model with Azure.

Managing variable compute costs through the design cycle

Using Azure can help customers optimize their costs as compute requirements will vary through the design cycle with lower requirements early on and peak demand near signoff. This is in contrast to the high fixed cost of on-premises infrastructure.

Running Voltus on Azure

We have used a block and full Chip test case to demonstrate our results.

The Azure team selected Edsv4 virtual machines (VMs) based on second-generation Intel Xeon Platinum 8272CL (Cascade Lake). These VMs are well suited for both compute and memory-intensive workloads.

The Voltus use case setup on Azure is illustrated in Figure 1.

Figure 1

High performance and elasticity

Voltus has a fully distributed and scalable architecture. Every step of the power integrity analysis flow, from design parsing to the solver, is fully distributed and scalable. Data from each part of the automatically partitioned design is assigned to compute nodes on the compute infrastructure for various steps in the analysis. This process is managed by a master machine as illustrated in Figure 2.

Figure 2

The level of distribution is user-controlled, which allows the user to take advantage of compute elasticity and manage performance. As Figure 3 illustrates for both the block and full chip run, we observe near-linear scalability in performance with respect to the number of CPUs.

Figure 3

Higher performance with lower costs

Believe it or not, that is indeed true. The elasticity of Voltus architecture enables the tool to run faster with a higher number of CPUs and since the CPUs are used for a smaller amount of time, the result is that the total cost drops to an optimal point. This can be seen at both the block and full chip levels as illustrated in Figure 3. This is a win-win situation where you can improve your performance and reduce your costs.

Figure 4

The magic of Voltus hierarchical analysis

Designers can further increase their performance and reduce cost by using Voltus XM hierarchical analysis. With Voltus XM, block-level models can be used instead of the full flattened design as illustrated in Figure 5. This method significantly reduces node count while maintaining accuracy. We can even further reduce our runtime and costs with Voltus XM and Azure. We observe a 4.5x reduction in cost and a 2x improvement in performance over the flat run for the full chip test case (Figure 6).

Figure 5

Figure 6

We have demonstrated the benefit of using Voltus on Azure at both the block level and chip level. These benchmarks show that customers can not only just benefit from higher performance using elastic compute, but there is an optimal point for performance and cost. Using Voltus XM hierarchical analysis further improves cost and performance. With Voltus on Azure, semiconductor companies have the ideal solution to verify power integrity for their most complex designs.

Learn more about Voltus on Azure

View our new high performance computing hub on Microsoft Docs
Read more about Azure HPC + AI

Please contact your Cadence sales representative for help enabling Voltus on Azure.

 

 

#AzureHPCAI
Quelle: Azure

da Agency

Microsoft Cost Management updates—November 2022

Whether you're a new student, a thriving startup, or the largest enterprise you have financial constraints, and you need to know what you're spending, where it’s being spent, and how to plan for the future. Nobody wants a surprise when it comes to the bill, and this is where Microsoft Cost Management comes in.

We're always looking for ways to learn more about your challenges and how Microsoft Cost Management can help you better understand where you're accruing costs in the cloud, identify and prevent bad spending patterns, and optimize costs to empower you to do more with less. Here are a few of the latest improvements and updates based on your feedback:

Use tag inheritance to group by subscription and resource group tags.
View cost change since previous period in the cost analysis preview.
New cost recommendations for virtual machine scale sets.
What's new in Cost Management Labs.
New ways to save money with Microsoft Cloud.
New videos and learning opportunities.
Documentation updates.
Join the Microsoft Cost Management team.

Let's dig into the details.

Use tag inheritance to group by subscription and resource group tags

As organizations grow their cloud usage, they want the ability to slice their cloud costs in multiple ways to better manage and optimize their cloud costs.

For example—finance teams may want costs grouped by department for cost allocation reasons, making each department responsible for the costs depending on their cloud usage. Engineering teams typically want to group costs by application or environment to understand where and how much they’re spending.

Tagging is an effective mechanism to group your costs but requires tagging every resource and relying on resource providers to support and emit tags with usage in the billing pipeline. To overcome these limitations and make it easier to use tags for cost reporting, you can now use the Cost Management tag inheritance preview to apply resource group and subscription tags to resource usage automatically in cost details.

With tag inheritance enabled, you can easily apply a single set of tags to your subscriptions rather than enforcing tag policies and tracking adoption, still to be left with some resources that don’t include tags in their usage data. This covers broad scenarios like departmental chargeback or environment. To tag lower-level data like applications, you can apply tags to each resource group.
Tag inheritance can be enabled on any Enterprise Agreement (EA) or Microsoft Customer Agreement (MCA) subscription. To enable tag inheritance across all subscriptions, enable it from the EA billing account or MCA billing profile.

You can enable tag inheritance in Cost Management from Cost analysis by selecting Configure at the top of the page or by opening Cost Management directly and selecting Manage billing account (or billing profile or subscription) from the menu. On the management settings page, you’ll see a Tag inheritance (preview) option with the current status.

Select Edit to enable tag inheritance and decide how to handle conflicts when tag names match. Once enabled, you should start to see inherited tags in cost details APIs and experiences, like Cost analysis and scheduled exports, in 8–24 hours.

To learn more, see Group and allocate costs using tag inheritance.

View cost change since previous period in the Cost analysis preview

Perhaps the most powerful aspect of the cloud is the flexibility it offers. But that flexibility comes at a cost–and while you can always get a good cost estimate from the Azure pricing calculator, most of us aren’t thinking about cost when we’re focused on solving a problem. This is where cloud computing becomes challenging–if we don’t understand the cost implications of the changes we make, we may very well get a surprise at the end of the month. To help spot these changes sooner, you can now find the percentage change since the previous period in the Cost analysis preview.

When your view is showing three months or less, the difference is calculated as the cost from the start of the period through yesterday, compared to the same days from the previous period. If showing more than three months, the date range uses the first month through the last month. If the current day or month are not part of the period you’re looking at (such as last month), the entire period is compared to the previous period.

Pair this with the average cost KPI and anomaly insights, and the Cost analysis preview gives you several new ways to catch unexpected changes in your cost patterns. If you aren’t using the Cost analysis preview yet, I recommend checking it out. We’re currently rolling out another change to help you start with the best view, so it would be good to share your thoughts early. Give the Cost analysis preview a shot and let us know what you think using the rating button at the bottom.

New cost recommendations for virtual machine scale sets

Cost optimization is on everyone’s minds these days. With a huge uptick in the usage of virtual machine scale sets (VMSS) over recent years, ensuring efficient use of VMSS resources is more important than ever. And as with virtual machines, one of the best ways to drive efficiency of VMSS is by right-sizing or deleting underutilized resources. To that end, Azure Advisor now includes cost optimization recommendations for VMSS.

Given the scale at which VMSS runs with multiple virtual machine instances, right-sizing is even more critical. So not only is it possible to over-provision the size or stock keeping unit (SKU) of the virtual machines, it’s also possible to over-provision the instances relative to the needs of the workloads running on these virtual machines. VMSS may also be used as the underlying infrastructure for Service Fabric, which has certain recommendations on the number of instances to be used, based on the reliability/durability tier.

Azure Advisor takes all these complexities into account while generating recommendations that are sure to save on your costs, while not impacting the performance or reliability of your workloads.

Overall, these recommendations represent close to $23 million in potential monthly savings! We want to help you do more with Azure for less.

To learn more, see Optimize virtual machine (VM) or virtual machine scale set (VMSS) spend.

What's new in Cost Management Labs

With Cost Management Labs, you get a sneak peek at what's coming in Microsoft Cost Management and can engage directly with us to share feedback and help us better understand how you use the service so we can deliver more tuned and optimized experiences. Here are a few features you can see in Cost Management Labs:

New: Change since previous period in the cost analysis preview—Now available in the public portal. 
Show the percentage difference in cost compared to the previous period at the top of the cost analysis preview. You can opt in using Try Preview.

New: Recent and pinned views in the cost analysis preview—Now enabled by default in Labs. 
Show all classic and preview views in the cost analysis preview and streamline navigation by prioritizing recently used and pinned views. You can see this in the Cost Management Labs or by opting in using Try Preview.
New: Recommendations view.
View a summary of cost recommendations that help you optimize your Azure resources in the cost analysis preview. You can opt in using Try Preview.
Forecast in the cost analysis preview.
Show your forecast cost for the period at the top of the cost analysis preview. You can opt in using Try preview.
Group related resources in the cost analysis preview.
Group related resources, like disks under VMs or web apps under App Service plans, by adding a “cm-resource-parent” tag to the child resources with a value of the parent resource ID.
Charts in the cost analysis preview. 
View your daily or monthly cost over time in the cost analysis preview. You can opt in using Try Preview.
View cost for your resources. 
The cost for your resources is one click away from the resource overview in the preview portal. Just click View cost to quickly jump to the cost of that resource.
Change scope from the menu.
Change scope from the menu for quicker navigation. You can opt-in using Try Preview.

Of course, that's not all. Every change in Microsoft Cost Management is available in Cost Management Labs a week before it's in the full Azure portal or Microsoft 365 admin center. We're eager to hear your thoughts and understand what you'd like to see next. What are you waiting for? Try Cost Management Labs today.

New ways to save money in the Microsoft Cloud

There were lots of cost optimization improvements happened over the last month. Here are some of the notable general availability offers you might be interested in:

Virtual Machine software reservations.
Azure Premium SSD v2 Disk Storage.
Auto-shutdown for Machine Learning compute instances.
New node sizing for Azure VMware Solution.
Azure Database for PostgreSQL in China North 3 and China East 3.
Azure Stream Analytics in Qatar Central.

And here are two new previews:

Azure HX and HBv4 virtual machines for HPC.
Azure Network Watcher for hybrid networks.

New videos and learning opportunities

Cost management and optimization were popular topics at Microsoft Ignite last month. Explore all 76 sessions with topics covering Azure, Microsoft 365, and more.

Follow the Microsoft Cost Management YouTube channel to stay in the loop with new videos as they’re released and let us know what you'd like to see next.

Want a more guided experience? Start with Control Azure spending and manage bills with Microsoft Cost Management.

Documentation updates

There were plenty of minor documentation updates. Here are a few you might be interested in:

New: Understand reservations discount for Azure SQL Edge.
New: Error when you create multiple subscriptions.
Updated: Overview of Cost Management + Billing–Complete rewrite to offer a more detailed overview.
Updated: How an Azure saving plan discount is applied–Covered how discounts are applied when both savings plans and reservations are available.
Updated: Azure portal administration for direct Enterprise Agreements–Added details about how to enable.
Updated: Reservation discount for Azure Data Explorer–Added details about stopping or suspending Data Explorer clusters.
Updated: Transfer Azure subscriptions between subscribers and CSPs–Added details about MCA subscription transfers.
9 updates based on your feedback.

Want to keep an eye on all documentation updates? Check out the Cost Management and Billing documentation change history in the azure-docs repository on GitHub. If you see something missing, select Edit at the top of the document and submit a quick pull request. You can also submit a GitHub issue. We welcome and appreciate all contributions.

Join the Microsoft Cost Management team

Are you excited about helping customers and partners better manage and optimize costs? We're looking for passionate, dedicated, and exceptional people to help build best in class cloud platforms and experiences to enable exactly that. If you have experience with big data infrastructure, reliable and scalable APIs, or rich and engaging user experiences, you'll find no better challenge than serving every Microsoft customer and partner in one of the most critical areas for driving cloud success.

Watch the video below to learn more about the Microsoft Cost Management team:

Join our team.

What's next?

These are just a few of the big updates from last month. Don't forget to check out the previous Microsoft Cost Management updates. We're always listening and making constant improvements based on your feedback, so please keep the feedback coming.

Follow @MSCostMgmt on Twitter and subscribe to the YouTube channel for updates, tips, and tricks. You can also share ideas and vote up others in the Cost Management feedback forum or join the research panel to participate in a future study and help shape the future of Microsoft Cost Management.

We know these are trying times for everyone. Best wishes from the Microsoft Cost Management team. Stay safe and stay healthy.
Quelle: Azure

da Agency

Any developer can be a space developer with the new Azure Orbital Space SDK

Earlier this year, we announced our vision to empower any developer to become a space developer through Azure. With over 90 million developers on GitHub, we have created a powerful ecosystem and we are focused on empowering the next generation of developers for space. Today, we are announcing a crucial step towards democratizing access to space development, with the preview release of Azure Orbital Space SDK (software development kit)—a secure hosting platform and application toolkit designed to enable developers to create, deploy, and operate applications on-orbit.

By bringing modern cloud-based applications to spacecrafts we not only increase the efficiency, value, and speed of insights from space data but also increase the value of that data through the optimization of ground communication.

Many of the fundamental technological improvements that have accelerated the growth of Internet of Things (IoT) in the past decade remain untapped by space development missions today. With the Azure Orbital Space SDK, we will help bring those improvements to space through modern agile software deployment, container-based development, use of higher-level languages, and cloud-managed networking. Extending the power of the Azure cloud into space means that spacecraft development will take less time, cost less, and bring more people into the space development ecosystem.

What is the Azure Orbital Space SDK?

The Azure Orbital Space SDK was created to be able to run on any spacecraft and provide a secure hosting platform and application kit to create, deploy, and operate applications on-orbit. This "host platform" runs onboard the spacecraft including a containerized, scalable compute infrastructure with resource and schedule management capabilities.

The application kit provides a set of templates, samples, and documentation to make it easy to get up and running as a space developer with template applications for common workload patterns, such as earth observation image processing. There is also a "virtual test harness" that allows developers to easily test their applications on the ground against an instance of the host platform.

How the Azure Orbital Space SDK is changing what’s possible

By moving the application onboard the spacecraft through the Azure Orbital Space SDK, we enable time and cost savings while radically altering and expanding the capabilities of the spacecraft.

Remote sensing

Remote sensing from space provides the perspective we need to better understand our world and powers commercial, economic, humanitarian, intelligence, and military scenarios—from damage assessments after weather events, to vessel detection, to crop monitoring and land classification.

Most remote sensing satellites have limited connectivity windows and bandwidth to communicate data back to the ground. As the fidelity of sensors increases, the amount of data they generate eclipses the available bandwidth. Being able to prioritize images that are useful, or even being able to send insights rather than the raw data down to the ground significantly reduces costs, accelerates speed, and fundamentally increases the value of the satellite.

Through the Azure Orbital Space SDK, developers can write and host more intelligent applications on-board satellites, meaning that they can capture data and use time more efficiently, and even autonomously reconfigure applications at the ultimate edge. Instead of building a unique solution each time developers deploy a spacecraft application, the Azure Orbital Space SDK creates a common template for performing imaging tasks, making it easier to transfer models and applications from one satellite configuration to another.

Communications

Satellite communications is one of the most well-known and widely used space capabilities. It allows us to watch live events around the world, provides internet and cloud connectivity to remote locations both on earth and in space, and supports the backbone of cellular networks. By bringing applications and intelligent computing on board satellites through the Azure Orbital Space SDK, we enable a more sophisticated management of satellite communications – resulting in lower costs and higher efficiency for satellite-based communication networks

Telecommunications networks have transitioned to software-defined networks and application–centric approaches to manage their communications infrastructures. The inclusion of satellites in 5G standards is the push for satellite networks to follow the same digital transformation. The Azure Orbital Space SDK will provide a compute fabric with networking capabilities for hosting telecommunication workloads, allowing operators to move applications more easily from ground-based cell sites to satellites in orbit, enabling better resiliency and network utilization.

Ultimately, by combining the Azure Orbital Space SDK with our portfolio of Azure Orbital products, we are bringing the power of cloud networking to the edge in space.

Azure Orbital Space SDK Partnerships

In April, we launched the Azure Space Partner Community and unveiled our initial cohort of space community partners, including Loft Orbital, Ball Aerospace and Thales Alenia Space. Today, we are announcing the newest member of our partner community—Xplore—who will help us continue to shape the future of space technologies and services.

Xplore

Xplore provides unique data including optical, video, and hyperspectral imagery via the XCRAFT, its highly capable, multi-sensor satellite. The XCRAFT's sophisticated sensors produce terabytes of data per day and will utilize powerful compute, storage, and communication solutions to deliver the unique insights derived to customers.

Microsoft and Xplore are partnering to use Azure Orbital Space SDK to gather new insights into how edge computing solutions can better enable both government and commercial customers to achieve their mission objectives. Together, our teams will investigate numerous on-orbit compute use-cases from downlink optimization to multi-sensor data fusion.

Loft Orbital

Loft Orbital is a space infrastructure and services company providing customers rapid, reliable, and simplified access to space. Loft has developed a highly modular satellite platform that enables them to provide a truly plug and play path to orbit for customer payloads and missions.

The Microsoft and Loft Orbital partnership will enable developers to easily develop, test, and deploy software-only “virtual payloads” to the Loft Orbital infrastructure. Together we are developing new technologies and products that will enhance the flexibility of on-orbit operations and provide seamless connectivity to the terrestrial cloud.

Earlier this year Microsoft and Loft conducted a successful test of demonstrating the integration of Loft spacecraft with the Azure Orbital Ground station.  Next year, we’ll build upon this success with the launch of YAM-6, a dedicated free-flying orbital testbed for customers to explore how our joint space infrastructure, connectivity, and on-orbit compute technologies will make access to space even easier than before.

Ball Aerospace

Ball Aerospace is a systems integrator with a heritage of designing and building government satellite programs and mission applications. Ball continues to innovate on behalf of its customers by combining their long expertise in exquisite satellite systems with modern tools and processes, enabling a more agile approach to space mission development and operations.

Together, Ball Aerospace and Microsoft are collaborating on the execution of series of on-orbit testbed satellites showcasing this highly agile future. These missions will leverage the Azure Orbital Space SDK to demonstrate modular and reconfigurable on-orbit processing technologies, necessary to support the complex missions for the United States Government.  The new software and hardware technologies demonstrated in these testbeds will unlock new capabilities for customers, granting the ability to support future concepts for smaller, agile, multi-mission capabilities across all federal space programs.

Thales Alenia Space

Thales Alenia Space is a leader in orbital infrastructures and is developing high-power, edge-computing solutions for space.

Microsoft is partnering with Thales Alenia Space to demonstrate and validate on-orbit compute technologies for multiple remote-sensing applications.   Our team’s future orbital testbed, launching to the International Space Station (ISS) in late 2023, brings together Thale’s edge computing hardware and Microsoft’s Azure Orbital Space SDK platform with visible and hyperspectral sensors, empowering the next generation to explore how space and on-orbit compute can improve our world. Developers on our platform will explore different on-orbit compute use cases, from AI-based hyperspectral image processing and to multi-sensor fusion algorithms, both computationally demanding workloads that benefit from Thales Alenia’s high-performance edge compute architecture.

In collaboration with Microsoft Research (MSR), Microsoft, and Thales Alenia Space, we are reducing the barriers for research in space through a range of outreach initiatives. One such initiative is the new Azure Space Academic Outreach program, that 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. The first pilots exploring this program are the University of Illinois Urbana-Champaign and NSF Spatiotemporal Innovation Center; however, we hope to open this up to more participants over the coming year.

What we’ve done and what’s coming next

The Azure Orbital Space SDK is a key part of the Azure Space portfolio and joins our investments together to create a value chain that is unique in the industry today—from space to ground to cloud. Over the past two years we’ve moved from a vision of combining the power of the cloud with the possibilities of space, into a reality with the launch of our our Azure Orbital Ground Station, the recently announced Azure Orbital Cloud Access, and today the Azure Orbital Space SDK.  Integral to Microsoft‘s approach across these announcements has been partnership, and we have partnered with space industry leaders to deliver incredible value to our customers, with most recently the partnership with DIU to support their hybrid space architecture and the development of the internet of space.

The Azure Orbital Space SDK will change what is possible onboard spacecraft, but also more importantly change the applications and insights we gather on earth and inform critical decisions and communications across the planet.

Learn more

For space companies interested in applying for preview access to Azure Orbital Space SDK, reach out to the Azure Space Partner Community. 
For universities interested in participating in on-orbit research for climate science, please reach out to the Azure Space Academic Outreach Program.
To learn more about Azure Space view our solution page.

Quelle: Azure

da Agency

Microsoft named a Leader in 2022 Gartner® Magic Quadrant™ for Full Life Cycle API Management

We are excited to share that Gartner® has positioned Microsoft as a Leader in the 2022 Magic Quadrant™ for Full Life Cycle API Management. This year’s placement marks the third consecutive year Microsoft has been recognized as a Leader. We believe our placement is a testament to our deep understanding of customer needs, strong customer adoption, positive feedback, and continued investments in building a differentiated platform.

Powering our customers’ digital transformation initiatives

APIs are critical to drive digital transformations in modern organizations. Thousands of the world’s largest enterprises trust Azure API Management to build, secure, and scale their API initiatives. With over a million APIs published on the Azure API Management platform today – it is a battle-hardened, production-ready, and highly scaled platform that stretches from on-premises to multicloud. Our customer use cases span a broad range from modernizing legacy applications to adopting API-first strategies to deliver innovations faster, create new revenue streams, and generate value for their customers and partners. Wegmans, a supermarket chain that re-invented the shopping experience in less than eight weeks, and Vipps, a leading Norwegian mobile payment provider that made mobile payments a norm, are examples of customers that are supercharging their digital transformation journey with the Azure API Management platform.

Delivering new capabilities for Azure API Management

Here are a few highlights of our latest features that are helping drive superior business outcomes for our customers around the world:

Support for new API types: Customers can now publish existing WebSocket and GraphQL backends as APIs in Azure API Management with high-fidelity experience in both Azure and the developer portal.
Support for hybrid and multicloud API management: To allow our customers to harness the power of hybrid or multicloud, we’ve enhanced the self-hosted gateway feature making it easier to efficiently and securely manage APIs hosted on-premises and across clouds from a single API Management service in Azure.
Security enhancements: Security is top of mind for all our customers, and we have added several new features—private links, managed certificates, authorizations to configure, store and swap authorization tokens, and more additions that help fortify their security and compliance posture.
Geographic expansion of existing Azure API Management availability regions: We have added four more regions to Europe and China, making Azure API Management available across 58 Azure regions.

Partnering for success on your digital transformation journey

Microsoft is committed to accelerating the pace of digital transformation for our customers.

Learn how organizations like yours use Azure API Management to accelerate their digital transformation journeys.
Download a complimentary copy of the 2022 Gartner Magic Quadrant for Full Life Cycle API Management to learn why Microsoft is named a Leader.

 

 

Gartner, Magic Quadrant for Full Life Cycle API Management, Shameen Pillai, Kimihiko Iijima, Mark O'Neill, John Santoro, Paul Dumas, Akash Jain, 14 November 2022.

Gartner and Magic Quadrant are registered trademarks and service marks of Gartner, Inc. and/or its affiliates in the U.S. and internationally and are used herein with permission. All rights reserved .

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.
Quelle: Azure

da Agency

Expanding AI technology for unstructured biomedical text beyond English

The health industry is embracing the power of big data, cloud computing, and clinical analytics, harnessing data to deliver insights that can improve care and efficiency. Still, unstructured text remains a challenge—made even more complex by barriers of language. Doctors’ notes and other unstructured text are often left unreferenced, are hard to parse and learn from, and are difficult to extract insights from, which leads to missed opportunities for diagnosis and better care.

Microsoft recognizes the need to enable healthcare organizations worldwide to gather insights from this data—for better, faster, and more personalized care, and to improve health equity. With Text Analytics for Health, a part of Azure Cognitive Services, healthcare organizations around the world can now extract meaningful insights from unstructured text in seven languages and process it in a way that enables clinical decision support like never before. Moving beyond English, Text Analytics for Health has now released six additional languages in preview—Spanish, French, German, Italian, Portuguese, and Hebrew—making this groundbreaking technology that helps extract insights from multilingual unstructured clinical notes accessible to more health organizations globally. This marks the first of its kind Natural Language Processing (NLP) service that holistically supports analysis of unstructured biomedical data in multiple languages and was developed with a federated learning approach. Most health technology is limited to the English language, making it inaccessible to millions of people and countries where English is not the primary language. Releasing NLP technology in multiple languages is a huge step forward in bridging the gaps in health equity created by language barriers and ensuring that access and quality of health care is not determined by one’s ability to speak and understand English.

Text Analytics for Health uses powerful NLP to detect and identify medical terms in text, classify them and associate them with standard clinical coding systems, as well as infer semantic relationships and assertions in the data, enabling deeper contextual understanding. This opens a world of possibilities for providers, payors, life sciences, and pharmaceutical companies, allowing them to unify data points from unstructured text with structured data, and enabling them to surface key insights, identify risks, automate form-filling, or match clinical trials to patients for better sourcing of candidates—based on comprehensive data including unstructured clinical text.

Training the NLP model for different languages

One of the challenges for an NLP service comes in moving past English—in aiming to analyze text from different languages. This is what Microsoft’s team aimed to do—the goal was to empower all health organizations, no matter the language their text is in. The unique challenges come from the need to train AI models for multiple languages, as well as adjust to country-specific needs. Syntax is different between languages, especially when it comes to non-Latin languages. Languages have different semantics and boundaries, especially those with rich morphology or compound words. Vocabularies are different, jargon is country-specific, and even coding systems differ by country. Words are often borrowed from other languages, leading to text that contains a mixture of multiple languages. Written text is a mixture of colloquialisms, local medical terms, and shorthand that is country-specific. Training models to understand these differences and then evaluating those models required significant amounts of clinical data and working with subject matter experts in different languages.

Leumit Health Services, one of the four national health funds in Israel, worked closely with Microsoft's R&D team to train the TA4H model for the Hebrew language. Israel has a unique and robust healthcare system where every individual’s records are stored in electronic medical records (EMR) and all citizen residents are required to join one of the four designated HMOs as per law. The health data available is rich, diverse, and provides a great starting point for research and analysis.

Leumit Health Services had over 130 million patient records in their EMR that could be used for training the Text Analytics for Health multilingual model for Hebrew. The challenge was—how to allow Microsoft access to de-identified data for training purposes in a manner that protected the privacy and security of the customer’s health information. The answer was in a Federated Learning approach—meaning data never left Leumit’s trust boundary and Microsoft was never exposed to patient’s health information. Leumit created a separate subscription in Azure with strict access permissions where Microsoft installed its federated learning infrastructure and tools. Leumit then put in de-identified data needed for the research and Microsoft developers triggered the model training in a federated learning setup on that de-identified data—all the while, this data never left their subscription, and the developers were never able to see any identifying details of the data.

Leumit then became one of the first customers to test the Text Analytics for Health model for clinical Hebrew, which is challenging since it often includes Hebrew and English words in the same sentence. The use case was trying to see if the Text Analytics for Health model could analyze free text from medical visits to identify predictors of strokes in patients. Preliminary results are very encouraging and positive—showing the model has ability to parse through both the Hebrew and English clinical statements and analyze them in a way that could help identify various potential indicators of stroke. This could help care providers set up early warning mechanisms and provide more personalized care for a variety of acute conditions.

“Using Microsoft’s Hebrew NLP, we will be able to analyze our 20 years of EMR data and patient-to-doctor messages to develop tools that will save physicians time and will reduce their burnout in a post-Covid-19 world."—Izhar Laufer, Head of Leumit Start.

Figure 1: Analysis of Hebrew unstructured biomedical text using Text Analytics for Health

Figure 2: Analysis of Hebrew unstructured biomedical text using Text Analytics for Health

 

Analyzing unstructured text for Real-World Data

The challenge of unstructured data is even greater in the research world with the use of Real-World Data (RWD). In Brazil, amongst other places, the lack of a standard for interoperability and data collection leads to a lot of unstructured data—field reports, doctors' notes, and even laboratory exam results. This slows down the process of research and analysis for providers such as Grupo Oncoclínicas. Founded in 2010, Grupo Oncoclínicas is the largest oncology treatment provider in the private sector in Brazil, with 129 units in 33 cities—including clinics, genomics and pathology laboratories, and integrated cancer treatment centers.

With the help of Dataside, a Microsoft partner in Brazil, OncoClinicas is using Microsoft’s Text Analytics for Health to extract data from non-structured fields like medical notes, anatomic pathology, and genomic and imaging reports like MRIs. This data is then used for various use cases such as clinical trial feasibility, a better understanding of the scenarios for pharmacoeconomics, and gaining a deeper understanding of group epidemiology and outcomes of interest.

Figure 3: Analysis of Portuguese unstructured biomedical text using Text Analytics for Health

“Text Analytics for Health was a turning point for Grupo Oncoclínicas to scale our processes and to structure our clinical notes, exam reports and field analysis, which previously only depended on manual curation. Having a solution that works in Portuguese is key—most global solutions tend to only cater to English, thereby neglecting other languages. Accuracy in the native Portuguese allowed us to maintain a high level of accuracy while analyzing the unstructured text.”—Marcio Guimaraes Souza, Head of Data and AI at Groupo OncoClinicas.

Analysis and structuring to Fast Healthcare Interoperability Resources (FHIR®)

The Italian Vita-Salute San Raffaele University and IRCCS San Raffaele Hospital are building the healthcare of the future by leveraging Microsoft’s Artificial Intelligence(AI) services. With Text Analytics for Health, the hospitals can classify, standardize, and analyze the enormous amount of clinical data available at the hospital in order to create an innovative digital platform for data management. Using this platform, the hospital’s physicians can gain important clinical insights about their patients and provide more personalized care. One of the use cases that is currently being developed using this data platform is for allowing the selection of patients eligible for immunotherapy for non-small cell lung cancer. Medical staff can leverage the analysis of AI solutions to increase the success rate of therapy by matching the relevant treatment to the most eligible patients.

“Text Analytics for Health has played a key role in analyzing the enormous amount of unstructured clinical data that we have at the hospital. We are also using the FHIR structuring capability, which allows greater interoperability with other hospital systems. Having Text Analytics for Health available in Italian now allows us to expand our capabilities even further to offer our patients the best possible care.”—Professor Carlo Tacchetti, Professor of Human Anatomy, Vita-Salute San Raffaele University, and coordinator of the project.

Figure 4: Analysis of Italian unstructured biomedical text using Text Analytics for Health

Do more with your data with Microsoft Cloud for Healthcare

With Text Analytics for Health, health organizations can transform their patient care, discover new insights and harness the power of machine learning and AI by leveraging unstructured text. Microsoft is committed to delivering technology that enables your data for the future of healthcare innovation with new features in the Microsoft Cloud for Healthcare.

We look forward to being your partner as you build the future of health.
•    Learn more about Text Analytics for Health.
•    Learn more about Microsoft Cloud for Healthcare.

®FHIR is a registered trademark of Health Level Seven International, registered in the U.S. Trademark Office, and is used with their permission.
Quelle: Azure

da Agency

NBA and Microsoft team up to transform fan experiences with cloud application modernization

There’s nothing quite like watching a basketball game and cheering on your favorite team as they battle it out for points before the buzzer sounds. From the players and employees to the technology, all need to work in lockstep to deliver a truly immersive experience.

As fans, we expect personalized experiences that bring the virtual world and the real world together on and off the court. This means brand new viewing experiences and virtual reality, real-time highlights of our favorite basketball games, and seamless ways to connect with other fans (and rivals!) when we want, how we want.

Having the right technology partner and cloud-based app transformation strategy is necessary to help organizations like the National Basketball Association (NBA) continue to deliver such unforgettable experiences and exceed fan expectations. Successful app modernization requires teamwork, which is why we’re proud to share our latest customer story featuring our partnership with the NBA.

Inside the customer playbook: NBA’s IT Application Development Group

Our latest customer story takes you into the world of the NBA’s IT Application Development Group, a dedicated team responsible for developing and maintaining the NBA's applications for internal and external users. The NBA leveraged Microsoft Azure application platform services for app modernization to accelerate the time to market of apps for multiple use cases that have elevated the NBA experience wherever fans, referees, and employees engage.

This process involved consolidating the apps and data the NBA was running from multiple locations into one place, including those that were on-premises. Modernizing a large app estate requires the NBA’s IT Application Development Group to plan for many tasks, from configuration and security to provisioning and scaling, and optimizing the networking and storage needs. Utilizing cloud technologies such as Azure App Service enabled the NBA to accelerate time to market by offloading these routine but important tasks to a fully managed application platform. They further streamlined the app development process with low-code and no-code capabilities using Azure and PowerApps.

How did this translate for fans, referees, and employees? Here’s a sneak peek of the use cases that you can read in detail in our customer story:

Fans: See how the NBA used virtual simulations and digital in-game experiences, to ensure fans felt connected to the game (and one another) when gathering in person was still difficult during the COVID-19 pandemic.

Referees (but really, fans!): Learn about REPS (Referee Engagement and Performance System), an app designed to aid referees and management in evaluation, collaboration, training, and development to ensure game consistency—and no bad calls.

Employees: Discover NBAOne, an internal mobile-first app the NBA created for its 1,800 employees consolidating no fewer than 50+ different applications into a single-sign-on experience. This simple-to-use app helped employees do everything from booking game tickets to marking time off, significantly improving their day-to-day employee experience.

Achieving a faster time to market

When it comes to delivering new experiences, we know that faster time to market is what keeps customers coming back. Azure brings not only the technology but also a number of fully managed services to support faster app and data modernization at scale:

Leverage fully managed application and data services such as Azure App Service, Azure Spring Apps, Azure SQL Database Hyperscale, and Azure Cosmos DB.
Quickly deploy line of business apps with low-code application development using Power Apps and Azure.
Build on containers with Azure Kubernetes Service (AKS).
Manage continuous deployment and development workstreams with AzureDevOps.
Get unmatched technical expertise through Microsoft United Support.

As a versatile platform with global scale, built-in security, and high availability, Azure is the all-star in your playbook to accelerate time-to-market with modern apps.

Choose your modern apps transformation strategy

Every customer is a potential fan, and when it comes to choosing the right technology partner, accelerating time to market, enabling higher productivity, and global scale are factors that deliver memorable customer experiences time and time again. We’re thrilled to have the NBA partner with Azure on this important mission and love the opportunity to this customer story.

Is your organization exploring app modernization? Learn more about Application and data modernization and how Azure can help you accelerate time to market to deliver incredible experiences.
Quelle: Azure

da Agency

AI and the need for purpose-built cloud infrastructure

The progress of AI has been astounding with solutions pushing the envelope by augmenting human understanding, preferences, intent, and even spoken language. AI is improving our knowledge and understanding by helping us provide faster, more insightful solutions that fuel transformation beyond our imagination. However, with this rapid growth and transformation, AI’s demand for compute power has grown by leaps and bounds, outpacing Moore’s Law’s ability to keep up. With AI powering a wide array of important applications that include natural language processing, robot-powered process automation, and machine learning and deep learning, AI silicon manufacturers are finding new, innovative ways to get more out of each piece of silicon such as integration of advanced, mixed-precision capabilities, to enable AI innovators to do more with less. At Microsoft, our mission is to empower every person and every organization on the planet to achieve more, and with Azure’s purpose-built AI infrastructure we intend to deliver on that promise.

Azure high-performance computing provides scalable solutions

The need for purpose-built infrastructure for AI is evident—one that can not only scale up to take advantage of multiple accelerators within a single server but also scale out to combine many servers (with multi-accelerators) distributed across a high-performance network. High-performance computing (HPC) technologies have significantly advanced multi-disciplinary science and engineering simulations—including innovations in hardware, software, and the modernization and acceleration of applications by exposing parallelism and advancements in communications to advance AI infrastructure. Scale-up AI computing infrastructure combines memory from individual graphics processing units (GPUs) into a large, shared pool to tackle larger and more complex models. When combined with the incredible vector-processing capabilities of the GPUs, high-speed memory pools have proven to be extremely effective at processing large multidimensional arrays of data to enhance insights and accelerate innovations.

With the added capability of a high-bandwidth, low-latency interconnect fabric, scale-out AI-first infrastructure can significantly accelerate time to solution via advanced parallel communication methods, interleaving computation and communication across a vast number of compute nodes. Azure scale-up-and scale-out AI-first infrastructure combines the attributes of both vertical and horizontal system scaling to address the most demanding AI workloads. Azure’s AI-first infrastructure delivers leadership-class price, compute, and energy-efficient performance today.

Cloud infrastructure purpose-built for AI

Microsoft Azure, in partnership with NVIDIA, delivers purpose-built AI supercomputers in the cloud to meet the most demanding real-world workloads at scale while meeting price/performance and time-to-solution requirements. And with available advanced machine learning tools, you can accelerate incorporating AI into your workloads to drive smarter simulations and accelerate intelligent decision-making.

Microsoft Azure is the only global public cloud service provider that offers purpose-built AI supercomputers with massively scalable scale-up-and-scale-out IT infrastructure comprised of NVIDIA InfiniBand interconnected NVIDIA Ampere A100 Tensor Core GPUs. Optional and available Azure Machine Learning tools facilitate the uptake of Azure’s AI-first infrastructure—from early development stages through enterprise-grade production deployments.

Scale-up-and-scale-out infrastructures powered by NVIDIA GPUs and NVIDIA Quantum InfiniBand networking rank amongst the most powerful supercomputers on the planet. Microsoft Azure placed in the top 15 of the Top500 supercomputers worldwide and currently, five systems in the top 50 use Azure infrastructure with NVIDIA A100 Tensor Core GPUs. Twelve of the top twenty ranked supercomputers in the Green500 list use NVIDIA A100 Tensor Core GPUs.

Source: Top 500 The List: Top500 November 2022, Green500 November 2022.

With a total solution approach that combines the latest GPU architectures, designed for the most compute-intensive AI training and inference workloads, and optimized software to leverage the power of the GPUs, Azure is paving the way to beyond exascale AI supercomputing. And this supercomputer-class AI infrastructure is made broadly accessible to researchers and developers in organizations of any size around the world in support of Microsoft’s stated mission. Organizations that need to augment their existing on-premises HPC or AI infrastructure can take advantage of Azure’s dynamically scalable cloud infrastructure.

In fact, Microsoft Azure works closely with customers across industry segments. Their increasing need for AI technology, research, and applications is fulfilled, augmented, and/or accelerated with Azure’s AI-first infrastructure. Some of these collaborations and applications are explained below:

Retail and AI

AI-first cloud infrastructure and toolchain from Microsoft Azure featuring NVIDIA are having a significant impact in retail. With a GPU-accelerated computing platform, customers can churn through models quickly and determine the best-performing model. Benefits include:

Deliver 50x performance improvements for classical data analytics and machine learning (ML) processes at scale with AI-first cloud infrastructure.
Leveraging RAPIDS with NVIDIA GPUs, retailers can accelerate the training of their machine learning algorithms up to 20x. This means they can use larger data sets and process them faster with more accuracy, allowing them to react in real-time to shopping trends and realize inventory cost savings at scale.
Reduce the total cost of ownership (TCO) for large data science operations.
Increase ROI for forecasting, resulting in cost savings from reduced out-of-stock and poorly placed inventory.

With autonomous checkout, retailers can provide customers with frictionless and faster shopping experiences while increasing revenue and margins. Benefits include:

Deliver better and faster customer checkout experience and reduce queue wait time.
Increase revenue and margins.
Reduce shrinkage—the loss of inventory due to theft such as shoplifting or ticket switching at self-checkout lanes, which costs retailers $62 billion annually, according to the National Retail Federation.

In both cases, these data-driven solutions require sophisticated deep learning models—models that are much more sophisticated than those offered by machine learning alone. In turn, this level of sophistication requires AI-first infrastructure and an optimized AI toolchain.

Customer story (video): Everseen and NVIDIA create a seamless shopping experience that benefits the bottom line.

Manufacturing

In manufacturing, compared to routine-based or time-based preventative maintenance, proactive predictive maintenance can get ahead of the problem before it happens and save businesses from costly downtime. Benefits of Azure and NVIDIA cloud infrastructure purpose-built for AI include:

GPU-accelerated compute enables AI at an industrial scale, taking advantage of unprecedented amounts of sensor and operational data to optimize operations, improve time-to-insight, and reduce costs.
Process more data faster with higher accuracy, allowing faster reaction time to potential equipment failures before they even happen.
Achieve a 50 percent reduction in false positives and a 300 percent reduction in false negatives.

Traditional computer vision methods that are typically used in automated optical inspection (AOI) machines in production environments require intensive human and capital investment. Benefits of GPU-accelerated infrastructure include:

Consistent performance with guaranteed quality of service, whether on-premises or in the cloud.
GPU-accelerated compute enables AI at an industrial scale, taking advantage of unprecedented amounts of sensor and operational data to optimize operations, improve quality, time to insight, and reduce costs.
Leveraging RAPIDS with NVIDIA GPUs, manufacturers can accelerate the training of their machine-learning algorithms up to 20x.

Each of these examples require an AI-first infrastructure and toolchain to significantly reduce false positives and negatives in predictive maintenance and to account for subtle nuances in ensuring overall product quality.

Customer story (video): Microsoft Azure and NVIDIA gives BMW the computing power for automated quality control.

As we have seen, AI is everywhere, and its application is growing rapidly. The reason is simple. AI enables organizations of any size to gain greater insights and apply those insights to accelerating innovations and business results. Optimized AI-first infrastructure is critical in the development and deployment of AI applications.

Azure is the only cloud service provider that has a purpose-built, AI-optimized infrastructure comprised of Mellanox InfiniBand interconnected NVIDIA Ampere A100 Tensor Core GPUs for AI applications of any scale for organizations of any size. At Azure, we have a purpose-built AI-first infrastructure that empowers every person and every organization on the planet to achieve more. Come and do more with Azure!

Learn more about purpose-built infrastructure for AI

Watch the Understanding AI and AI Infrastructure webcast.
Read the An AI-First Infrastructure and Toolchain for Any Scale whitepaper.
Read the Accelerating AI and HPC in the Cloud whitepaper.
Learn more about Azure HPC + AI.
Keep up to date on the Azure + NVIDIA partnership and offerings. 

Quelle: Azure