What’s new in Azure Data, AI, & Digital Applications: Modernize your data estate, build intelligent apps, and apply AI solutions

I write this blog each month to help navigate through the intense pace of news and innovation we’re releasing for customers. And what a year we’ve had! We witnessed incredible advances at breathtaking speed as AI reshaped what’s possible across industries, organizations, and our day-to-day lives.

Personally, I’m loving the features and capabilities of Copilot for Microsoft 365, especially meeting summaries after a Teams call so I can quickly access the most important points and any actions. We’re still experimenting with AI to craft marketing narratives across my team and it’s exciting to see how AI enhances our own creativity.

AI technology is not brand new. It’s been improving experiences across applications and business processes for some time but, the broad availability of generative AI models and tools this year was a watershed moment.  

Customers and partners made quick pivots to bring AI into their transformation roadmaps, and within months were deploying applications and services powered by AI. I have never seen a new technology generate such profound change at this pace. It shows how ready many organizations were for this moment; how investments made in the cloud, data, DevOps, and creating transformation cultures set the stage for AI adoption. This year Microsoft introduced hundreds of resources, models, services, and tools to help customers and partners maximize AI.

This month, I’m pleased to expand this blog’s scope and bring in what’s new for digital applications for a holistic look at everything we’re delivering to help customers modernize their data estate, build intelligent applications, and apply AI technologies to help achieve their business goals. Let’s go.

New models and multimodal capabilities available in Azure AI

Our focus is to deliver the most cutting-edge open and frontier models available so developers can build with confidence and unlock immediate value across their organization.  

Last month we announced a significant expansion of Azure OpenAI Service and introduced Models as a Service (MaaS). This is a way for model providers to offer their latest open and frontier LLMs on Azure for generative AI developers to integrate into their applications.

And just last week, we announced the availability of MaaS for Llama 2. With MaaS for Llama 2, developers can integrate with their favorite LLM tools like Prompt Flow, Semantic Kernel, and LangChain with a ready-to-use API and pay-as-you go billing based on tokens for LLMs. This allows generative AI developers to access Llama 2 via hosted fine-tuning without provisioning GPUs, greatly simplifying the model set up and deployment process. Then they can offer their custom applications utilizing Llama 2, purchased through and hosted on the Azure Marketplace.

Model catalog in Azure AI Studio Preview

We continue to advance our Azure OpenAI Service and recently launched several multimodal AI capabilities that empower businesses to build generative AI experiences with image, text and video, including:

• DALL·E 3, in preview: Generate images from text descriptions. DALL·E 3 is a remarkable AI model that does just that. Users describe an image, and DALL·E 3 will be able to create it.
• GPT-3.5 Turbo model with a 16k token prompt length, generally available, and GPT-4 Turbo, in preview: The latest models in Azure OpenAI Service enable customers to extend prompt length and bring more control and efficiency to their generative AI applications.
• GPT-4 Turbo with Vision (GPT-4V), in preview: When integrated with Azure AI Vision, GPT-4V enhances experiences by allowing the inclusion of images or videos along with text for generating text output, benefiting from Azure AI Vision enhancement like video analysis.
• Fine-tuning of Azure OpenAI Service models: Fine-tuning is now generally available for Azure OpenAI Service models including Babbage-002, Davinci-002, and GPT-35-Turbo. Developers and data scientists can customize these Azure OpenAI Service models for specific tasks.
• GPT-4 updates: Azure OpenAI Service has also rolled out updates to GPT-4, including the ability for fine-tuning. Fine-tuning will allow organizations to customize the AI model to better suit their specific needs. It’s akin to tailoring a suit to fit perfectly, but in the world of AI. Updates to GPT-4 are in preview.

Steering at the frontier: Extending the power of prompting

The power of prompting in GPT-4 continues to amaze! A recent Microsoft Research blog discusses promptbase, an approach to prompting GPT-4 that harnesses its powerful reasoning abilities. Across a wide variety of test sets (including the ones used to benchmark the recently announced Gemini Ultra), GPT-4 gets better results than other AI models (including Gemini Ultra). And it does this with zero-shot chain-of-thought prompting.

Infuse Responsible AI (RAI) tools and practices in your LLMOps

As AI adoption matures and companies put their AI apps into production, it’s important to consider the safety boundaries supporting the short- and long-term ROI of those applications. This month, we continued our series on LLMOps for business leaders with an article focused on how to infuse responsible AI into your AI development lifecycle. We highlight best practices and tools in Azure AI Studio to help development teams put their principles into practice.

Azure AI Advantage offer

Azure Cosmos DB is the cloud database for the Era of AI. It supports built-in AI, including natural language queries, AI vector search capabilities, and simple AI integration with Azure AI Search. We’re investing in helping customers discover these benefits with our new Azure AI Advantage offer, which helps new and existing Azure AI and GitHub Copilot customers save when using Azure Cosmos DB by providing 40,000 Request Units per second (RU/s) of Azure Cosmos DB for 90 days.

What’s new in the Azure data platform—because AI is only as good as your data

Every intelligent app starts with data so a modern data and analytics platform is essential for any AI transformation.

One example of this how multinational law firm Clifford Chance leverages new technologies to benefit their clients. The firm built a solid data platform on Azure to innovate with new technologies, including the new generation of large language models, Azure OpenAI and Microsoft 365 Copilot. Early innovations are already delivering value with cognitive translation proving to be one of the fastest growing products their IT team has ever released.

And Belfius, a Belgian insurance company, built on the Microsoft intelligent data platform using services like Azure Machine Learning and Azure Databricks to reduce development time, increase efficiency, and gain reliability. As a result, their data scientists can focus on creating and transforming features and the company can better detect fraud and money laundering. 

Azure and Databricks: Co-innovation for powerful AI experiences

Microsoft’s Scott Guthrie (left) and Arun Ulagaratchagan (right) with Databrick’s Co-Founder and CEO Ali Ghodsi (center) at Ignite

At Microsoft Ignite 2023 in November the benefits of maturing AI tools and services were in full view as customers and partners shared how Microsoft is empowering them to achieve more. 

Databricks is of one of our most strategic partners with some of the fastest growing data services on Azure. We recently showcased our co-innovation, including Azure Databricks interoperability with Microsoft Fabric, and how Azure Databricks is taking advantage of Azure OpenAI to deliver AI experiences for Azure Databricks’ customers. This means customers can take advantage of LLMs in Azure OpenAI as they build AI capabilities like retrieval-augmented generation (RAG) applications on Azure Databricks, and then use Power BI in Fabric to analyze the output.

If you missed Databricks’ CEO Ali Ghodsi on stage with Scott Guthrie at Ignite, I encourage you to check out the replay of Scott’s full segment Microsoft Cloud in the era of AI. If you only have a few minutes, his wrap up on LinkedIn is a great option, too. It’s a helpful overview of how the Microsoft Cloud is uniquely positioned to empower customers to transform by building AI solutions and unlocking data insights using the same platform and services that power all of Microsoft’s comprehensive solutions.

And for a closer look at our latest work with Databricks, check out the click-thru version of the Modern Analytics with Microsoft Fabric and Azure Databricks DREAM Lab from Ignite.

Now in preview: Azure AI extension for Azure Database for PostgreSQL 

The new Azure AI extension allows developers to leverage large language models (LLMs) in Azure OpenAI to generate vector embeddings and build rich, PostgreSQL generative AI applications. These powerful new capabilities combined with existing support for the pgvector extension, make Azure Database for PostgreSQL another great destination for building AI-powered apps. 

Azure Arc brings cloud innovation to SQL Server anywhere

This month, we’re introducing a new set of enhanced manageability and security capabilities from SQL Server enabled by Azure Arc. With Monitoring for SQL Server, customers can gain critical insights into their entire SQL Server estate and optimize for database performance. Customers can also view and manage Always On availability groups, failover cluster instances, and backups directly from the Azure portal, with better visibility and simplicity. Lastly, with Extended Security Updates as a service and automated patching, customers can always keep their apps secure, compliant, and up to date at all times. 

Lower pricing for Azure SQL Database Hyperscale compute

New pricing on Azure SQL Database Hyperscale offers cloud-native workloads the performance and security of Azure SQL at the price of commercial open-source databases. Hyperscale customers can save up to 35% on the compute resources they need to build scalable, AI-ready cloud applications of any size and I/O requirement. The new pricing is now available.

Digital applications deliver transformational operations and experiences

This era of AI is brought to life through digital applications developed and deployed by companies putting AI to work to enhance their operations and experiences—like a personalized app experience for employees or a customized chatbot for end customers. Here are some recent updates that help make all this innovation possible.

The seven pillars of modern AI development: Leaning into the era of custom copilots

Copilots are generating a lot of excitement and with Azure AI Studio in public preview, developers have a platform purpose-built for generative AI application development. As we lean into this new era, it’s important for businesses to carefully consider how to design a durable, adaptable, and effective approach. How can AI developers ensure their solutions enhance customer engagement? Here are seven pillars to think through when building your custom copilot.

AKS is a leading platform for modern cloud native and intelligent applications

The future of app development is at the intersection of AI and cloud-native technologies like Kubernetes. Cloud-native and AI are deeply rooted together in fueling innovation at scale and Azure Kubernetes Service (AKS) provides the scale customers need to run their compute intensive workloads like AI and machine learning. Check out Brendan Burn’s recent blog from KubeCon to learn how Microsoft is building and servicing open-source communities that benefit our customers.

There are no limits to your innovation with Azure

It has been amazing to see the tech community and customer response to our recent news, resources, and features across the portfolio, particularly digital applications. 

Resources like the Platform Engineering Guide launched in with Ignite have been incredibly popular, demonstrating the demand and appetite for this kind of training material.  

Seeing how organizations innovate with the technology is what it’s all about.   

Here are a couple of customer stories that caught my attention recently.

Modernizing interactive experiences across LEGO House with Azure Kubernetes Service

We’re collaborating with The LEGO House in Denmark—the ultimate LEGO experience center for children and adults—to migrate custom-built interactive digital experiences from an aging on-prem data center to Microsoft Azure Kubernetes Service (AKS) to improve stability, security, and the ability to iterate and collaborate on new guest experiences. This shift to the cloud enables LEGO House to more quickly update these experiences as they learn from guests. As the destination modernizes it hopes to share learnings and technologies with the broader LEGO Group ecosystem, like LEGOLAND and brand retail stores. 

Gluwa chose Azure for a reliable, scalable cloud solution to bring banking to emerging, underserved markets and close the financial gap

An estimated 1.4 billion people lack access to basic financial services because they live in a country with limited financial infrastructure, making it difficult to get credit or personal and business loans. Gluwa with Creditcoin uses blockchain technology to differentiate their business through borderless financial technology and chose Azure as the foundation to support it. Using a wide combination of our services and solutions—.NET framework, Azure Container Instances, AKS, Azure SQL, Azure Cosmos DB, and more—Gluwa has a strong platform to support their offerings. The business has also boosted operational efficiency with reliable uptime, stable services, and rich product offerings. 

CARIAD creates a service platform for Volkswagen Group vehicles with Azure and AKS

With the automotive industry shifting to software defined vehicles, CARIAD, the Volkswagen Group software subsidiary, collaborated with Microsoft using Azure and AKS to create the CARIAD Service Platform for providing automotive applications to brands like Audi, Porsche, Volkswagen, Seat and Skoda. This platform powers and accelerates the development and service of vehicle software by CARIAD’s developers, helping software become an advantage for the Volkswagen Group in the next generation of automotive mobility.

DICK’S Sporting Goods creates an omnichannel customer experience using Azure Arc and AKS

To create a more consistent, personalized experience for customers across its 850 stores and its online retail experience, DICK’S Sporting Goods envisioned a “one store” technology strategy with the ability to write, deploy, manage, and monitor its store software across all locations nationwide—and reflect those same experiences through its eCommerce site. DICK’S needed a new level of modularity, integration, and simplicity to seamlessly connect its public cloud environment with its computing systems at the edge. With the help of Microsoft, DICK’s Sporting Goods is migrating its on-premise infrastructure to Microsoft Azure and creating an adaptive cloud environment comprised of Azure Arc and Azure Kubernetes Service (AKS). Now the retailer can easily deploy new applications to every store to support a ubiquitous experience. 

Azure Cobalt delivers performance and efficiency for intelligent applications

Azure provides hundreds of services supporting the performance demands of cloud native and intelligent applications. Our work to maximize performance and efficiency now extends to the silicon powering Azure. We recently introduced Azure Maia, our first custom AI accelerator series to run cloud-based training and inferencing for AI workloads, and our custom in-house CPU series, Azure Cobalt, the first CPU designed by us, specifically for the Microsoft Cloud. 

Cobalt 100, the first generation in the series, is a 64-bit 128-core chip that delivers up to 40% performance improvement over current generations of Azure Arm chips and can power services such as Microsoft Teams and Azure SQL.

With Cobalt, we will deliver performance and economics to meet the demands of resource-intensive workloads like intelligent applications using generative AI for at-scale workloads. Our customized silicon and system features include dynamic power capabilities that can be tuned per-core based on the workload, leveraging the co-optimization of hardware with software to deliver best-in-class performance efficiency. 

Don’t just take it from me and Omar. Rani Borkar, CVP, Azure Hardware Systems shares how Cobalt delivers differentiated performance—and can even help drive toward achieving sustainability goals:

“We’re building sustainability into every part of our hardware for the cloud, from our silicon to the servers. This starts in the design phase, and on Cobalt we made those intentional design choices to be able to control performance and power consumption per core and on every single VM. We’re pleased with the performance we’ve seen testing Cobalt on internal workloads like Teams and Azure SQL, and looking forward to rolling this out more widely to customers as a VM offering next year.”

Grow your data, AI, and intelligent applications understanding and skillset

I often use December downtime to do a deep dive into a new technology or learn a new skill. I’m a data geek, and I have fond memories from a few years ago when I worked through more than a dozen Power BI trainings and loved every minute of it. Demystifying AI and helping customers build the right skillsets is just one way Microsoft is empowering AI transformation. Here are some new learning resources if you are like me and plan to end the year learning something new!

AI in a Minute: Looking for help ramping your teams on generative AI? Or maybe you want to go to end of year gatherings prepared to discuss AI and its possibilities? Our new “AI in a Minute” video series explains generative AI basics in short, snackable bites anyone can digest, regardless of job tile, level, or industry.

GenAI for beginners: This free 12-lesson course is designed to teach beginners everything they need to know to start building with generative AI. 

Microsoft Learn Cloud Skills Challenge—Microsoft Ignite Edition: Skill up for in-demand Data & AI tech scenarios and enter to win a VIP pass to the next Microsoft Ignite or Microsoft Build by completing a challenge by January 15, 2024. 

Cloud Workshop for the SQL Professional—If you missed this workshop at the PASS Data Summit, the labs are available to go through at your own pace. Our next workshop will be in March at the Microsoft Fabric Community Conference, so be sure to register and come see us in Las Vegas.

Accelerate your AI journey with key solution accelerators—Get started building intelligent apps on Azure with newly-published demos, GitHub repos, and Hackathon content to create AI-powered, intelligent apps. Customers and partners can “Build Your Own Copilot” using a click-through demo, sellers and partners can run a Hackathon with customers, and developers can leverage a code repo to quickly build solutions in their Azure account.

What will you build in 2024? Transform your business with a trusted partner

The ability of AI to accelerate transformation across industries, organizations, and daily life will certainly continue at an intense pace in 2024. Microsoft is proud to be your trusted partner in this era of AI and we’re committed to helping you achieve more for your business. I am excited to see how data, AI, and digital applications innovation unfolds for your business in the new year!

Source: microsoft.com

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Build next-generation, AI-powered applications on Microsoft Azure

The potential of generative AI is much bigger than any of us can imagine today. From healthcare to manufacturing to retail to education, AI is transforming entire industries and fundamentally changing the way we live and work. At the heart of all that innovation are developers, pushing the boundaries of possibility and creating new business and societal value even faster than many thought possible. Trusted by organizations around the world with mission-critical application workloads, Azure is the place where developers can build with generative AI securely, responsibly, and with confidence.

Welcome to Microsoft Build 2023—the event where we celebrate the developer community. This year, we’ll dive deep into the latest technologies across application development and AI that are enabling the next wave of innovation. First, it’s about bringing you state-of-the-art, comprehensive AI capabilities and empowering you with the tools and resources to build with AI securely and responsibly. Second, it’s about giving you the best cloud-native app platform to harness the power of AI in your own business-critical apps. Third, it’s about the AI-assisted developer tooling to help you securely ship the code only you can build.

We’ve made announcements in all key areas to empower you and help your organizations lead in this new era of AI.

Bring your data to life with generative AI

Generative AI has quickly become the generation-defining technology shaping how we search and consume information every day, and it’s been wonderful to see customers across industries embrace Microsoft Azure OpenAI Service. In March, we announced the preview of OpenAI’s GPT-4 in Azure OpenAI Service, making it possible for developers to integrate custom AI-powered experiences directly into their own applications. Today, OpenAI’s GPT-4 is generally available in Azure OpenAI Service, and we’re building on that announcement with several new capabilities you can use to apply generative AI to your data and to orchestrate AI with your own systems.

We’re excited to share our new Azure AI Studio. With just a few clicks, developers can now ground powerful conversational AI models, such as OpenAI’s ChatGPT and GPT-4, on their own data. With Azure OpenAI Service on your data, coming to public preview, and Azure Cognitive Search, employees, customers, and partners can discover information buried in the volumes of data, text, and images using natural language-based app interfaces. Create richer experiences and help users find organization-specific insights, such as inventory levels or healthcare benefits, and more.

To further extend the capabilities of large language models, we are excited to announce that Azure Cognitive Search will power vectors in Azure (in private preview), with the ability to store, index, and deliver search applications over vector embeddings of organizational data including text, images, audio, video, and graphs. Furthermore, support for plugins with Azure OpenAI Service, in private preview, will simplify integrating external data sources and streamline the process of building and consuming APIs. Available plugins include plugins for Azure Cognitive Search, Azure SQL, Azure Cosmos DB, Microsoft Translator, and Bing Search. We are also enabling a Provisioned Throughput Model, which will soon be generally available in limited access to offer dedicated capacity.

Customers are already benefitting from Azure OpenAI Service today, including DocuSign, Volvo, Ikea, Crayon, and 4,500 others.

We continue to innovate across our AI portfolio, including new capabilities in Azure Machine Learning, so developers and data scientists can use the power of generative AI with their data. Foundation models in Azure Machine Learning, now in preview, empower data scientists to fine-tune, evaluate, and deploy open-source models curated by Azure Machine Learning, models from Hugging Face Hub, as well as models from Azure OpenAI Service, all in a unified model catalog. This will provide data scientists with a comprehensive repository of popular models directly within the Azure Machine Learning registry.

We are also excited to announce the upcoming preview of Azure Machine Learning prompt flow that will provide a streamlined experience for prompting, evaluating, tuning, and operationalizing large language models. With prompt flow, you can quickly create prompt workflows that connect to various language models and data sources. This allows for building intelligent applications and assessing the quality of your workflows to choose the best prompt for your case. See all the announcements for Azure Machine Learning.

It’s great to see momentum for machine learning with customers like Swift, a member-owned cooperative that provides a secure global financial messaging network, who is using Azure Machine Learning to develop an anomaly detection model with federated learning techniques, enhancing global financial security without compromising data privacy. We cannot wait to see what our customers build next.

Run and scale AI-powered, intelligent apps on Azure

Azure’s cloud-native platform is the best place to run and scale applications while seamlessly embedding Azure’s native AI services. Azure gives you the choice between control and flexibility, with complete focus on productivity regardless of what option you choose.

Azure Kubernetes Service (AKS) offers you complete control and the quickest way to start developing and deploying intelligent, cloud-native apps in Azure, datacenters, or at the edge with built-in code-to-cloud pipelines and guardrails. We’re excited to share some of the most highly anticipated innovations for AKS that support the scale and criticality of applications running on it.

To give enterprises more control over their environment, we are announcing long-term support for Kubernetes that will enable customers to stay on the same release for two years—twice as long as what’s possible today. We are also excited to share that starting today, Azure Linux is available as a container host operating system platform optimized for AKS. Additionally, we are now enabling Azure customers to access a vibrant ecosystem of first-party and third-party solutions with easy click-through deployments from Azure Marketplace. Lastly, confidential containers are coming soon to AKS, as a first-party supported offering. Aligned with Kata Confidential Containers, this feature enables teams to run their applications in a way that supports zero-trust operator deployments on AKS.

Azure lets you choose from a range of serverless execution environments to build, deploy, and scale dynamically on Azure without the need to manage infrastructure. Azure Container Apps is a fully managed service that enables microservices and containerized applications to run on a serverless platform. We announced, in preview, several new capabilities for teams to simplify serverless application development. Developers can now run Azure Container Apps jobs on demand and schedule applications and event-driven ad hoc tasks to asynchronously execute them to completion. This new capability enables smaller executables within complex jobs to run in parallel, making it easier to run unattended batch jobs right along with your core business logic. With these advancements to our container and serverless products, we are making it seamless and natural to build intelligent cloud-native apps on Azure.

Integrated, AI-based tools to help developers thrive

Making it easier to build intelligent, AI-embedded apps on Azure is just one part of the innovation equation. The other, equally important part is about empowering developers to focus more time on strategic, meaningful work, which means less toiling on tasks like debugging and infrastructure management. We’re making investments in GitHub Copilot, Microsoft Dev Box, and Azure Deployment Environments to simplify processes and increase developer velocity and scale.

GitHub Copilot is the world’s first at-scale AI developer tool, helping millions of developers code up to 55 percent faster. Today, we announced new Copilot experiences built into Visual Studio, eliminating wasted time when getting started with a new project. We’re also announcing several new capabilities for Microsoft Dev Box, including new starter developer images and elevated integration of Visual Studio in Microsoft Dev Box, that accelerates setup time and improves performance. Lastly, we’re announcing the general availability of Azure Deployment Environments and support for HashiCorp Terraform in addition to Azure Resource Manager.

Enable secure and trusted experiences in the era of AI

When it comes to building, deploying, and running intelligent applications, security cannot be an afterthought—developer-first tooling and workflow integration are critical. We’re investing in new features and capabilities to enable you to implement security earlier in your software development lifecycle, find and fix security issues before code is deployed, and pair with tools to deploy trusted containers to Azure.

We’re pleased to announce GitHub Advanced Security for Azure DevOps in preview soon. This new solution provides the three core features of GitHub Advanced Security into the Azure DevOps platform, so you can integrate automated security checks into your workflow. It includes code scanning powered by CodeQL to detect vulnerabilities, secret scanning to prevent the inclusion of sensitive information in code repositories, and dependency scanning to identify vulnerabilities in open-source dependencies and provide update alerts.

While security is at the top of the list for any developer, using AI responsibly is no less important. For almost seven years, we have invested in a cross-company program to ensure our AI systems are responsible by design. Our work on privacy and the General Data Protection Regulation (GDPR) has taught us that policies aren’t enough; we need tools and engineering systems that help make it easy to build with AI responsibly. We’re pleased to announce new products and features to help organizations improve accuracy, safety, fairness, and explainability across the AI development lifecycle.

Azure AI Content Safety, now in preview, enables developers to build safer online environments by detecting and assigning severity scores to unsafe images and text across languages, helping businesses prioritize what content moderators review. It can also be customized to address an organization’s regulations and policies. As part of Microsoft’s commitment to responsible AI, we’re integrating Azure AI Content Safety across our products, including Azure OpenAI Service and Azure Machine Learning, to help users evaluate and moderate content in prompts and generated content.

Additionally, the responsible AI dashboard in Azure Machine Learning now supports text and image data in preview. This means users can more easily identify model errors, understand performance and fairness issues, and provide explanations for a wider range of machine learning model types, including text and image classification and object detection scenarios. In production, users can continue to monitor their model and production data for model and data drift, perform data integrity tests, and make interventions with the help of model monitoring, now in preview.

We are committed to helping developers and machine learning engineers apply AI responsibly, through shared learning, resources, and purpose-built tools and systems.

Let’s write this history, together

AI is a massive shift in computing. Whether it is part of your workflow or part of cloud development, powering your next-generation, intelligent apps, this community of developers is leading this shift. 

We are excited to bring Microsoft Build to you, especially this year as we go deep into the latest AI technologies, connect you with experts from within and outside of Microsoft, and showcase real-world solutions powered by AI.

Source: microsoft.com

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Preparing for future health emergencies with Azure HPC

A once-in-a-century global health emergency accelerates worldwide healthcare innovation and novel medical breakthroughs, all supported by powerful high-performance computing (HPC) capabilities.

COVID-19 has forever changed how nations function in the globally interconnected economy. To this day, it continues to affect and shape how countries respond to health emergencies. COVID-19 has demonstrated just how interconnected our society is and how risks, threats, and contagions can have global implications for many aspects of our daily lives.

COVID-19 was the largest global health emergency in over a century, with nearly 762 million cases reported as of the end of March 2023, according to the World Health Organization. The National Centre for Biotechnology Information points out the frequency and breath of new variants that continues to emerge at regular intervals. In response to this intricate health crisis, the global healthcare community quickly mobilized to better understand the virus, learn its behavior, and work toward preventative treatment measures to minimize the damage to lives across the world. Globally, nations mobilized resources for frontline workers, offered social protection to those most severely affected, and provided vaccine access for the billions who need it.

Recent technological innovations have provided the medical community with access to capabilities, such as HPC, that equipped healthcare professionals to better study, understand, and respond to COVID-19. Globally, healthcare innovators could access unprecedented computing power to design, test, and develop new treatments, faster, better, and more iteratively, than ever before.

Today, Azure HPC enables researchers to unleash the next generation of healthcare breakthroughs. For example, the computational capabilities offered by the Azure HPC HB-series virtual machines, powered by AMD EPYCTM CPU cores, allowed researchers to accelerate insights and advances into genomics, precision medicine, and clinical trials, with near-infinite high-performance bioinformatics infrastructure capabilities.

Since the beginning of COVID-19, companies have been leveraging Azure HPC to develop new treatments, run simulations, and testing at scale—all in preparation for the next health emergency. Azure HPC is helping companies unleash new treatments and health cure capabilities that are ushering in the next generation of treatments and healthcare capabilities, across the entire industry.

High-performance computing making a difference

A leading immunotherapy company partnered with Microsoft to leverage the capabilities of Azure HPC’s high-performance computing, in order to perform detailed computational analyses of the spike protein structure of SARS-CoV-2. Due to the critical nature of the spike protein structure and the role it plays in allowing the invasion of human cells, targeting it for study, analyses, and insights, is a crucial step in the development of treatments to combat the virus.

The company’s engineers and scientists collaborated with Microsoft, and quickly deployed HPC clusters on Azure, containing over 1250 core graphic processing units (GPUs). These GPUs are specifically designed for machine learning and similarly intense computational applications. The Azure HPC clusters augmented the company’s existing GPU clusters—which was already optimized for molecular modelling of proteins, antibodies, and antivirals—bringing a truly high-powered scaled engagement approach to fruition.

By collaborating with Microsoft in this way and making use of the massive, networked computing capabilities and advanced algorithms enabled by Azure HPC, the company was able to generate working models in days rather than the months it would have taken by following traditional approaches.

The incredible amount of computing power will help bolster drug discovery and therapeutic developments. By joining forces and bringing together the incredible power of Azure HPC and cutting edge immunotherapies, it helped contribute to the development of models that allowed researchers to better understand the virus, find novel binding sites to fight the virus, and ultimately guide the development of future treatments and vaccines for the virus.

Powering pharmaceutical research and innovation

The healthcare industry is making remarkable strides in the development of cutting-edge treatments and innovations that are geared towards solving some of the world's greatest healthcare challenges.

For example, researchers are leveraging HPC to transform their research and development effort as well as accelerating the development of new life-saving treatments.

Using a technique producing amorphous solid dispersions (ASD), drug researchers break up active pharmaceutical ingredients and blend them with organic polymers to improve the dissolution rate, bioavailability, and solubility of drug delivery systems. Although a wonder of modern medicine, it is a highly complicated, often lab-based process that can take months.

Swiss-based Molecular Modelling Laboratory (MML), a leader in ASD screening, wanted to pivot its drug research and development to small organic and biomolecular polymers. This approach determines ASD stability prior to formulation, reveals new ASD combinations, enhances drug safety, and helps reduce drug development costs as well as delivery times.

MML chose to leverage Azure HPC resources on more than 18,000 Azure HBv2 virtual machines and to optimize high-throughput drug screening and active pharmaceutical ingredient solubility limit detection, with the aim to alleviate common development hurdles.

The adoption of Azure HPC has helped MML shift from a small start-up to an established business working with some of the top pharmaceutical companies in the world—all in a very short time.

For the global healthcare community, the computational power and scalability of Azure HPC presents an unprecedented opportunity to accelerate pharmaceutical, medical, as well as health innovation. Azure HPC will continue playing a leading role in supporting the healthcare industry to respond optimally to any future global health emergency that may arise.

Source: microsoft.com

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How 5G and wireless edge infrastructure power digital operations with Microsoft

As enterprises continue to adopt the Internet of Things (IoT) solutions and AI to analyze processes and data from their equipment, the need for high-speed, low-latency wireless connections are rapidly growing. Companies are already seeing benefits from deploying private 5G networks to enable their solutions, especially in the manufacturing, healthcare, and retail sectors.

The potential of 5G and multi-access edge computing (MEC) has evolved substantially. As they are fully ready to enable the next-generation of digital operations, it is important to highlight some recent successful deployments that provide high speeds and ultra-low latency.

These findings have been included in the latest Digital Operations Signals report. Where our previous industry trends report, IoT Signals, gave insight for audiences into IoT, we thought it was important for this latest report to go beyond IoT and into the world of digital operations. The report now encompasses the business outcomes that organizations are pursuing to unlock the next level of improvements in efficiency, agility, and sustainability in their physical operations utilizing AI, machine learning, digital twins, 5G, and more.

As 5G connections and mobile edge computing continue to advance, so does the demand for its adoption. Interestingly, the Digital Operations Signals report found that cloud radio access networks (C-RAN), private Wi-Fi networks, and MEC technologies are not just continuing to develop, but they are also likely to converge. This means we could see more unified on-site network architectures with faster, more powerful computing.

What can 5G infrastructure deliver?

Traditionally, local connectivity in business sites—such as hospitals, clinics, warehouses, and factories—was provided by Ethernet and Wi-Fi. While Wi-Fi is still in common use for enterprise on-premises connections, it doesn’t always offer the bandwidth, latency, security, and reliability needed for demanding IoT solutions, particularly for rugged operational environments. The wider availability of 5G connectivity is spurring growth in new edge solutions and an increasing number of IoT device connections. It is now possible to have higher throughput and latency as low as 100 milliseconds or less for a device to respond to a hosting server’s request.

But the adoption of 5G is more than just a network upgrade. Instead, it’s ushering in a new category of network-intelligent applications that can solve problems that were once out of reach. With 5G, you can deploy edge applications based on cloud-native distributed architecture for solutions that demand low latency and dedicated quality of service. By using 5G and leveraging APIs to interact with networks, these applications can deliver high-performing, optimized experiences.

How is 5G being used by enterprises today?

In factory settings, for example, AI requires low latency to improve control processes and robotic systems, recognize objects through advanced computer vision, and effectively manage warehouse and supply chain operations. In this scenario, 5G and MEC can help power computer vision-assisted product packing and gather near-real-time data on any mistakes. This opens the potential to improve on-site quality assurance for logistics and supply chain companies and reduce processing times.

In healthcare, 5G connections support AI’s use in medical diagnoses, health monitoring, predictive maintenance and monitoring of medical systems, and telemedicine applications. In retail operations, low-latency connections allow AI to help with real-time inventory management, in-store video traffic, and in-store real-time offers.

The 5G architecture consists of three different network tiers—low band, midband, and millimeter wave (mmWave) high band—that offer different advantages and disadvantages in coverage distances and speed. Additionally, key 5G services specialize in providing different features:

◉ Enhanced mobile broadband (eMBB): By defining a minimum level of data transfer rate, eMBB can provide ultra-high wireless bandwidth capabilities, handling virtual reality, computer vision, and large-scale video streaming.

◉ Massive machine-type communications (mMTC): Designed for industrial scenarios and other environments requiring numerous devices to be connected to each other, mMTC could be used with IoT solutions or large spaces with a variety of devices that would need to communicate together.

◉ Ultra-reliable low-latency communications (URLLC): This is designed for use cases that require extremely low latency and high reliability. This would benefit situations where responsiveness is critical, such as public safety and emergency response uses, remote healthcare, industrial automation, smart energy grids, and controlling autonomous vehicles.

Using these services to achieve high speeds and performance, however, requires businesses to upgrade network technology and update their older wireless and edge architectures. To help overcome these challenges, enterprises are turning to the right combination of hardware, software, and cloud services that can optimize 5G at the edge.

How are Microsoft and Intel empowering 5G solutions?

Microsoft and Intel understand the many challenges that enterprises face. By working with telecom hyper scalers, independent solution providers, and other partners, we are providing 5G infrastructure and network services that are easily adaptable for use cases in many sectors. Azure private multi-access edge compute (MEC) helps operators and system integrators simplify the delivery of ultra-low-latency solutions over 4G and 5G netwworks. By reducing integration complexity, enterprises can innovate new solutions and generate new revenue streams.

Intel has designed a range of hardware to power 5G edge network activities and improve content transmission and processing. By providing foundational technology to run 5G, they are working to help standardize and simplify its use and create more unified edge applications and services. By helping customers securely and efficiently deploy 5G across industries, they can reap the benefits of 5G without complicated or extended timelines.

Source: microsoft.com

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Azure Data Manager for Energy: Achieve interoperability with Petrel

Microsoft Azure Data Manager for Energy is the first fully managed OSDU™ Data Platform built for the energy industry. This solution is the first step in unraveling the challenge of data—moving from disparate systems and disconnected applications to a holistic approach. The product’s ideation directly reflects the partnership between Microsoft and SLB, capitalizing on each organization’s unique expertise.

As the energy industry works to achieve a sustainable low carbon future, organizations are taking advantage of the cloud to optimize existing assets and de-risk new ventures. Universally, data is at the core of their digital transformation strategies—yet only a small fraction of energy company data is properly tagged and labeled to be searchable. This leads engineers and geoscientists to spend significant time outside of their expertise trying to discover and analyze data. Azure Data Manager for Energy customers can seamlessly connect to an open ecosystem of interoperable applications from other Independent Software Vendors (ISVs) and the Microsoft ecosystem of productivity tools. Ultimately, the open Microsoft platform enables developers, data managers, and geoscientists alike to innovate the next generation of digital solutions for the energy industry.

Enhanced data openness and liberation in Petrel

“We all benefit from making the world more open. As an industry, our shared goal is that openness in data will enable a fully liberated and connected data landscape. This is the natural next step towards data-driven workflows that integrate technologies seamlessly and leverage AI for diverse and creative solutions that take business performance to the next level.”—Trygve Randen, Director, Data & Subsurface at Digital & Integration, SLB.

The co-build partnership between Microsoft and SLB improves customers’ journey and performance, by unlocking data through interoperable applications. Delfi™ digital platform from SLB on Azure features a broad portfolio of applications, including the Petrel E&P Software Platform. The Petrel E&P Software Platform enhanced with AI enables workflows in Petrel to run with significantly faster compute times and include access to new tools, increasing the flexibility and productivity of geoscientists and engineers.

Microsoft and SLB rearchitected Petrel Data Services to allow Petrel Projects and data to be permanently stored in the customer’s instance. Petrel Data Services leverages core services found in OSDU™, such as partition and entitlement services. This change further aligns Petrel Data Services with the OSDU™ Technical Standard schemas and directly integrates with storage as the system of record. Now when geoscientists or engineers create new Petrel projects or domain data, each is liberated from Petrel into its respective Domain Data Management Service (DDMS) provided by OSDU™, like seismic or wellbore, in Azure Data Manager for Energy. These Petrel liberated projects or data become immediately discoverable in Petrel on Delfi™ Digital Platform or any third-party application developed in alignment with the emerging requirements of the OSDU™ Technical Standard such as INT’s IVAAP.

By splitting Petrel and Project Explorer software as a service (SaaS) applications from the data infrastructure, data resides in Azure Data Manager for Energy without any dependencies on an external app to access that data. Users can access and manage Petrel liberated Project Explorer and data in Azure Data Manager for Energy independent of any prerequisite application or license. Microsoft provides a secure, scalable infrastructure that governs data safely in the customer tenant while SLB focuses on delivering continuous updates to Petrel and Project Explorer on Delfi™ Digital Platform which expedites feature delivery.

Petrel and Project Explorer on Azure Data Manager for Energy

1. Search for and Discover Petrel Projects: Petrel Project Explorer shows all Petrel Project versions liberated from all users and allows the viewing of data associated with each project based on corresponding data entitlements. This includes images of the windows that are created in the project, metadata (coordinate reference systems, time zone, and more), and all data stored in the project. Using Project Explorer allows to preserve every single change throughout the lifetime of a Petrel project and preserve every critical milestone required by regulations or for historical purposes. Data and decisions can be easily shared and connected to other cloud native solutions on Delfi™ Digital Platform, and automatic, full data lineage and project versioning is always available.

2. Connect Petrel to domain data: Petrel users can consume seismic and wellbore OSDU™ domain data directly from Azure Data Manager for Energy. Furthermore, Petrel Data Services enables the development of diverse and creative solutions for the exploration and production value chain which includes liberated data consumption in other applications like Microsoft Power BI for data analytics.

3. Data liberation: Petrel Data Services empowers Petrel users to liberate Petrel Project data into Azure Data Manager for Energy where data and project content can be accessed without opening Petrel, providing simpler data overview and in-context access. Data liberation allows for direct consumption into other data analytics applications, generating new data insights into Petrel projects, breaking down data silos, and improving user and corporate data-driven workflows. It relieves users from Petrel project management and improves the focus on domain workflows for value creation.

Figure 1: Project Explorer on Azure Data Manager for Energy: View all Petrel projects within an organization in one place through an intuitive and performant User Interface (UI).

Interoperable workflows that de-risk and drive efficiency

Both traditional and new energy technical workflows are optimized when data and application interoperability are delivered. Geoscientists and engineers, therefore, want to incorporate as much diverse domain data as possible. Customers want to run more scenarios in different applications, compare results with their colleagues, and ultimately liberate the best data and the knowledge associated with it to a data platform for others to discover and consume. With Petrel and Petrel Data Services powered by Azure Data Manager for Energy, customers achieve this interoperability.

Companies can liberate wellbore and seismic data for discovery in any application developed in alignment with the emerging requirements of the OSDU™ Technical Standard. As Petrel and Petrel Data Services use the standard schemas, all data is automatically preserved and indexed for search, discovery, and consumption. This extensibility model enables geoscientists and engineers as well as data managers to seamlessly access data in their own internal applications. SLB apps on Delfi™ Digital Platform such as Techlog, as well as Microsoft productivity tools including Power BI and an extensive ecosystem of partner apps are all available in this model. Additionally, developers can refocus their efforts on innovating and building new apps—taking advantage of Microsoft Power Platform to build low-code or no-code solutions. This creates the full data-driven loop and ultimately enables integrated workflows for any interoperable apps.

Figure 2: Azure Data Manager for Energy Data Flow connects seamlessly to a broad ecosystem of interoperable applications across Delfi™, Azure Synapse, Microsoft Power Platform, and the ISV ecosystem.

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Azure previews powerful and scalable virtual machine series to accelerate generative AI

Delivering on the promise of advanced AI for our customers requires supercomputing infrastructure, services, and expertise to address the exponentially increasing size and complexity of the latest models. At Microsoft, we are meeting this challenge by applying a decade of experience in supercomputing and supporting the largest AI training workloads to create AI infrastructure capable of massive performance at scale. The Microsoft Azure cloud, and specifically our graphics processing unit (GPU) accelerated virtual machines (VMs), provide the foundation for many generative AI advancements from both Microsoft and our customers.

“Co-designing supercomputers with Azure has been crucial for scaling our demanding AI training needs, making our research and alignment work on systems like ChatGPT possible.”—Greg Brockman, President and Co-Founder of OpenAI. 

Azure's most powerful and massively scalable AI virtual machine series

Today, Microsoft is introducing the ND H100 v5 VM which enables on-demand in sizes ranging from eight to thousands of NVIDIA H100 GPUs interconnected by NVIDIA Quantum-2 InfiniBand networking. Customers will see significantly faster performance for AI models over our last generation ND A100 v4 VMs with innovative technologies like:

◉ 8x NVIDIA H100 Tensor Core GPUs interconnected via next gen NVSwitch and NVLink 4.0

◉ 400 Gb/s NVIDIA Quantum-2 CX7 InfiniBand per GPU with 3.2Tb/s per VM in a non-blocking fat-tree network

◉ NVSwitch and NVLink 4.0 with 3.6TB/s bisectional bandwidth between 8 local GPUs within each VM

◉ 4th Gen Intel Xeon Scalable processors

◉ PCIE Gen5 host to GPU interconnect with 64GB/s bandwidth per GPU

◉ 16 Channels of 4800MHz DDR5 DIMMs

Delivering exascale AI supercomputers to the cloud

Generative AI applications are rapidly evolving and adding unique value across nearly every industry. From reinventing search with a new AI-powered Microsoft Bing and Edge to AI-powered assistance in Microsoft Dynamics 365, AI is rapidly becoming a pervasive component of software and how we interact with it, and our AI Infrastructure will be there to pave the way. With our experience of delivering multiple-ExaOP supercomputers to Azure customers around the world, customers can trust that they can achieve true supercomputer performance with our infrastructure. For Microsoft and organizations like Inflection, NVIDIA, and OpenAI that have committed to large-scale deployments, this offering will enable a new class of large-scale AI models.

"Our focus on conversational AI requires us to develop and train some of the most complex large language models. Azure's AI infrastructure provides us with the necessary performance to efficiently process these models reliably at a huge scale. We are thrilled about the new VMs on Azure and the increased performance they will bring to our AI development efforts."—Mustafa Suleyman, CEO, Inflection.

AI at scale is built into Azure’s DNA. Our initial investments in large language model research, like Turing, and engineering milestones such as building the first AI supercomputer in the cloud prepared us for the moment when generative artificial intelligence became possible. Azure services like Azure Machine Learning make our AI supercomputer accessible to customers for model training and Azure OpenAI Service enables customers to tap into the power of large-scale generative AI models. Scale has always been our north star to optimize Azure for AI. We’re now bringing supercomputing capabilities to startups and companies of all sizes, without requiring the capital for massive physical hardware or software investments.

“NVIDIA and Microsoft Azure have collaborated through multiple generations of products to bring leading AI innovations to enterprises around the world. The NDv5 H100 virtual machines will help power a new era of generative AI applications and services.”—Ian Buck, Vice President of hyperscale and high-performance computing at NVIDIA. 

Today we are announcing that ND H100 v5 is available for preview and will become a standard offering in the Azure portfolio, allowing anyone to unlock the potential of AI at Scale in the cloud.

Source: azure.microsoft.com

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Microsoft shares what's next in machine learning at NVIDIA GTC

Finding scalable solutions for today’s global challenges requires forward-thinking, transformative tools. As environmental, economic, and public health concerns mount, Microsoft Azure is addressing these challenges head on with high-performance computing (HPC), AI, and machine learning. The behind-the-scenes power for everything from MRI scans to energy management and financial services, these technologies are equipping customers and developers with innovative solutions that break through the boundaries of what’s possible in data and compute, paving the way for growth opportunities that span industries and applications around the world.

Microsoft Azure is committed to unlocking these new opportunities for our customers, providing the broadest range of NVIDIA GPUs at the edge, on-premises, in the cloud, and for hybrid environments.

At NVIDIA GTC we will demonstrate this commitment by showing how Azure’s advanced HPC capabilities, and AI/machine learning in the cloud are driving transformation and making an impact together with NVIDIA’s latest technology.

Microsoft Azure’s collaboration with NVIDIA was developed with our customers in mind and focused on opening new doors to innovation with graphics processing unit (GPU) acceleration in the cloud.

Learn more by registering today for NVIDIA GTC, a free, online event running September 19 to 22, 2022.

Get a chance to win an NVIDIA Jetson Nano or swag box

In both of our sessions you have a chance to win a SWAG box complete with a HPC t-shirt and mug or a Jetson Nano. Attend these sessions and don’t forget to look for the special link to enter!

Microsoft Sessions at NVIDIA GTC

The new SDK and CLI in Azure Machine Learning.

Bala Venkataraman, Principal Program Manager, Microsoft.

Video on demand

Azure Machine Learning is committed to simplifying the adoption of its platform for training and production. In 2022, we announced the general availability of Azure Machine Learning CLI v2 and the preview of Azure Machine Learning Python SDK v2. Both launches demonstrate our continued focus on making workflows easier and managing their entire lifecycle starting from training single jobs to pipelines and model deployments. In this session, learn about the key improvements in usability and productivity, and the new features that come with the command-line interpreter (CLI) and software development kit (SDK) v2 of Azure Machine Learning.

◉ Register for this session now.

Operationalize large model training on Azure Machine Learning using multi-node NVIDIA A100 GPUs.

Sharmeelee Bijlani, Program Manager Azure Machine Learning, Microsoft; Razvan Tanase, Principal Engineering Manager Azure Machine Learning, Microsoft.

Wednesday, September 21, 10:00 to 10:50 AM PDT (1:00 to 1:50 PM EDT, 7:00 to 7:50 AM CEST)

In recent years, deep learning models have grown exponentially in size, demonstrating an acute need for customers to train and fine-tune them using large-scale data infrastructure, advanced GPUs, and an immense amount of memory. Fortunately, developers can now use simple training pipelines on Azure Machine Learning to train large models running on the latest multi-node NVIDIA GPUs. This session will describe these software innovations to customers through Azure Machine Learning (including a fully optimized PyTorch environment) that offers great performance and an easy-to-use interface for large-scale training. We’ll also highlight the power of Azure Machine Learning through experiments using 1,024 A100 Tensor Core GPUs to scale the training of a two-trillion parameter model with a streamlined user experience at 1,000 plus GPU scale.

◉ Register for this session now.

Watch Party #1: Operationalize large-model training on Azure Machine Learning using multi-node NVIDIA A100 GPUs.

Mary Howell, NVIDIA.

Wednesday, Sep 21st, 3:00 - 3:30 PM PDT

In this GTC Watch Party, we will be replaying our Operationalize Large-Model Training on Azure Machine Learning using Multi-Node NVIDIA A100 GPUs session. Participants will be joined by experts from across Microsoft and NVIDIA who bring fresh insights and experiences to the table, taking the session to a whole new level of understanding. Interaction is core to our GTC Watch Parties, and we encourage you to join the discussion with any comments or questions. 

◉ Register for this session.

Watch Party #2: Operationalize large-model training on Azure Machine Learning using multi-node NVIDIA A100 GPUs.

Gabrielle Davelaar, AI Technical Specialist, Microsoft; Maxim Salnikov, Senior Azure GTM Manager, Microsoft; Henk Boelman, Senior Cloud Advocate–AI and Machine Learning, Microsoft; Alexander Young, Technical Marketing Engineer, NVIDIA; Ulrich Knechtel, Microsoft Partner Manager (EMEA), NVIDIA.

Thursday, September 22, 2:00 to 3:30 PM CEST (5:00 to 6:30 AM PDT, 8:00 to 9:30 AM EDT)

In this GTC Watch Party, we will be replaying our Operationalize Large-Model Training on Azure Machine Learning using Multi-Node NVIDIA A100 GPUs session. Participants will be joined by experts from across Microsoft and NVIDIA who bring fresh insights and experiences to the table, taking the session to a whole new level of understanding. Interaction is core to our GTC Watch Parties, and we encourage you to join the discussion with any comments or questions.

◉ Register for this session now.

Microsoft is helping customers across industries step up, transforming AI and machine learning at the Edge

Nuance’s Dragon Ambient eXperience helps doctors document care faster with AI on Azure

Nuance developed an AI-based clinical solution that automatically turns doctor-patient conversations into accurate medical notes. Built with Azure and PyTorch, this solution saves doctors transcription time, reducing administrative burdens and helping them conduct more focused, higher-quality interactions with their patients.

Energy utility Elva builds a highly secure DevOps platform with Azure infrastructure and network security services

Elva looked to build a secure, cloud-first DevOps platform that could meet Norway’s data residency and compliance requirements, delivering automated services that would help develop network grid technology. Using Azure DDoS Protection, Azure Web Application Firewall, and Azure Kubernetes Service, Elva realized its goal, enhancing its in-house development and data integration capabilities. 

The Royal Bank of Canada creates personalized offers while protecting data privacy with Azure confidential computing

The Royal Bank of Canada (RBC) partnered with Microsoft to create a privacy-preserving multi-party data sharing platform built on Azure confidential computing. Called VCR, this solution enables RBC to personalize offerings and protect privacy at the same time, creating exceptional digital experiences that clients can trust.  

Recapping 2022 moments with Azure and NVIDIA technologies

Azure NC A100 v4-series

At Microsoft, our NC series virtual machines allow customers access to almost limitless AI hardware infrastructure so they can be productive quickly. Last summer, we leveled up, announcing the general availability of Azure NC A100 v4 series virtual machines. Powered by NVIDIA A100 80GB PCle Tensor Core GPUs and 3rd Gen AMD EPYC™ processors, these virtual machines help our customers gain insights faster, innovate with speed, do more with less, and are the most performant and cost-competitive NC series offering for a diverse set of workloads.

DeepSpeed on Azure

Azure Machine Learning uses large fleets of the latest NVIDIA GPUs powered by NVIDIA Quantum InfiniBand interconnects to tackle large-scale AI training and tuning. Last July, we announced a breakthrough in our software stack, using DeepSpeed and 1,024 NVIDIA A100 GPUs to scale the training of a two trillion parameter model with a streamlined user experience at 1,000 plus GPU scale. We are bringing these software innovations to you through Azure Machine Learning (including a fully optimized PyTorch environment) that offers great performance and an easy-to-use interface for large-scale training.

NVads A10 v5 virtual machines

Traditionally, graphics-heavy visualization workloads that run in the cloud require virtual machines with full GPUs that are both costly and inflexible. To combat this, we introduced the first GPU-partitioned (GPU-P) virtual machine offering in the cloud, and just last July, we announced the general availability of NVads A10 v5 GPU accelerated virtual machines. Azure is the first public cloud to offer GPU partitioning on NVIDIA GPUs, and our new NVads A10 v5 virtual machines are designed to offer the right choice for any workload and provide optimum configurations for both single-user and multi-session environments. Dig into our latest virtual machine innovation.

NVIDIA Jetson AGX Orin-powered edge AI devices now available

Microsoft is pleased to announce that the NVIDIA Jetson AGX Orin SoM is now powering Azure Certified edge devices from industry-leading device builders including AAEON, Advantech, and AVerMedia, along with the NVIDIA Jetson AGX Orin developer kit.

Developers and solution builders can now leverage powerful NVIDIA Jetson AGX Orin devkits and production modules with Microsoft Azure to create, deploy, and operate powerful AI solutions at the edge, accelerating product development and deployment at scale. The NVIDIA Orin Nano modules have set a new baseline for entry-level edge AI and robotics, building on the momentum behind the Jetson Orin platform worldwide. Stay tuned for new Jetson Orin NX and Orin Nano partner products launching to meet customer needs in AI solution development.

NVIDIA DLI training powered by Azure

We’re proud to host NVIDIA deep learning institute (DLI) training at NVIDIA GTC again this year, with instructor-led workshops around accelerated computing, accelerated data science, and deep learning. Hosted on Microsoft Azure, these sessions enable and empower you to leverage NVIDIA GPUs on the Microsoft Azure platform to solve the world’s most interesting and relevant problems. Register for a DLI workshop today.

Join us at NVIDIA GTC

In collaboration with NVIDIA, Microsoft delivers purpose-built AI, machine learning, and HPC solutions in the cloud to meet even the most demanding real-world applications at scale. Join us at NVIDIA GTC September 19 to 22, to see how every enterprise can leverage the power of GPUs at the edge, on-premises, in the cloud, and for hybrid solutions.

Source: microsoft.com

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