This week we’re announcing news and advancements to showcase our commitment to your success in this dynamic era. Let’s get started.

Introducing Microsoft Azure AI Foundry: A unified platform to design, customize, and manage AI solutions 

Every new generation of applications brings with it a changing set of needs, and just as web, mobile, and cloud technologies have driven the rise of new application platforms, AI is changing how we build, run, govern, and optimize applications. According to a Deloitte report, nearly 70% of organizations have moved 30% or fewer of their Generative AI experiments into production—so there is a lot of innovation and results ready to be unlocked. Business leaders are looking to reduce the time and cost of bringing their AI solutions to market while continuing to monitor, measure, and evaluate their performance and ROI.

This is why we’re excited to unveil Azure AI Foundry today as a unified application platform for your entire organization in the age of AI. Azure AI Foundry helps bridge the gap between cutting-edge AI technologies and practical business applications, empowering organizations to harness the full potential of AI efficiently and effectively.

We’re unifying the AI toolchain in a new Azure AI Foundry SDK that makes Azure AI capabilities accessible from familiar tools, like GitHub, Visual Studio, and Copilot Studio. We’ll also evolve Azure AI Studio into an enterprise-grade management console and portal for Azure AI Foundry.

Azure AI Foundry is designed to empower your entire organization—developers, AI engineers, and IT professionals—to customize, host, run, and manage AI solutions with greater ease and confidence. This unified approach simplifies the development and management process, helping all stakeholders focus on driving innovation and achieving strategic goals.

For developers, Azure AI Foundry delivers a streamlined process to swiftly adapt the latest AI advancements and focus on delivering impactful applications. Developers will also find an enhanced experience, with access to all existing Azure AI Services, and tooling along with new capabilities we’re announcing today. 

For IT professionals and business leaders, adopting AI technologies raises important questions about measurability, ROI, and ongoing optimization. There’s a pressing need for tools that provide clear insights into AI initiatives and their impact on the business. Azure AI Foundry enables leaders to measure their effectiveness, align them with organizational goals, and more confidently invest in AI technologies.

To help you scale AI adoption in your organization, we’re introducing comprehensive guidance for AI adoption and architecture within Azure Essentials so you are equipped to successfully navigate the pace of AI innovation. Azure Essentials gives you access to Microsoft’s best practices, product experiences, reference architectures, skilling, and resources into a single destination. It’s a great way to benefit from all we’ve learned and the approach you’ll find aligns directly with how to make the most of Azure AI Foundry.

In a market flooded with disparate technologies and choices, we created Azure AI Foundry to thoughtfully address diverse needs across an organization in the pursuit of AI transformation. It’s not just about providing advanced tools, though we have those, too. It’s about fostering collaboration and alignment between technical teams and business strategy.

Now, let’s dive into additional updates designed to enhance the overall experience and efficiency throughout the AI development process, no matter your role.

Introducing Azure AI Agent Service to automate business processes and help you focus on your most strategic work  

AI agents have huge potential to autonomously perform routine tasks, boosting productivity and efficiency, all while keeping you at the center. We’re introducing Azure AI Agent Service to help developers orchestrate, deploy, and scale enterprise AI-powered apps to automate business processes. These intelligent agents handle tasks independently, involving human users for final review or action, ensuring your team can focus on your most strategic initiatives. 

A standout feature of Agent Service is the ability to easily connect enterprise data for grounding, including Microsoft SharePoint and Microsoft Fabric, and tools integration to automate actions. With features like bring your own storage (BYOS) and private networking, it ensures data privacy and compliance, helping organizations protect their sensitive data. This allows your business to leverage existing data and systems to create powerful and secure agentic workflows.

Enhanced observability and collaboration with a new management center experience

To support the development and governance of generative AI apps and fine-tuned models, today we’re unveiling a new management center experience right in Azure AI Foundry portal. This feature brings essential subscription information, such as connected resources, access privileges, and quota usage, into one pane of glass. This can save development teams valuable time and facilitate easier security and compliance workflows throughout the entire AI lifecycle. 

Expanding our AI model catalog with more specialized solutions and customization options 

From generating realistic images to crafting human-like text, AI models have immense potential, but to truly harness their power, you need customized solutions. Our AI model catalog is designed to provide choice and flexibility and ensure your organization and developers have what they need to explore what AI models can do to advance your business priorities. Along with the latest from OpenAI and Microsoft’s Phi family of small language models, our model catalog includes open and frontier models. We offer more than 1,800 options and we’re expanding to offer even more tailored and specialized task and industry-specific models.  

We’re announcing additions that include models from Bria, now in preview, and NTT DATA, now generally available. Industry-specific models from Bayer, Sight Machine, Rockwell Automation, Saifr/Fidelity Labs, and Paige.ai are also available today in preview for specialized solutions in healthcare, manufacturing, finance, and more.

We’ve seen Azure OpenAI Service consumption more than double over the past six months, making it clear customers are excited about this partnership and what it offers_². We look forward to bringing more innovation to you with our partners at OpenAI, starting with new fine-tuning capabilities like vision fine-tuning and distillation workflows which allow a smaller model like GPT-4o mini to replicate the behavior of a larger model such as GPT-4o with fine-tuning, capturing its essential knowledge and bringing new efficiencies.

Along with unparalleled model choice, we equip you with essential tools like benchmarking, evaluation, and a unified model inference API so you can explore, compare, and select the best model for your needs without changing a line of code. This means you can easily swap out models without the need to recode as new advancements emerge, ensuring you’re never locked into a single model.

New collaborations to streamline model customization process for more tailored AI solutions

We’re announcing collaborations with Weights & Biases, Gretel, Scale AI, and Statsig to accelerate end-to-end AI model customization. These collaborations cover everything from data preparation and generation to training, evaluation, and experimentation with fine-tuned models. 

The integration of Weights & Biases with Azure will provide a comprehensive suite of tools for tracking, evaluating, and optimizing a wide range of models in Azure OpenAI Service, including GPT-4, GPT-4o, and GPT-4o-mini. This ensures organizations can build AI applications that are not only powerful, but also specifically tailored to their business needs.  

The collaborations with Gretel and Scale AI aim to help developers remove data bottlenecks and make data AI-ready for training. With Gretel Azure OpenAI Service integration, you can upload Gretel generated data to Azure OpenAI Service to fine-tune AI models and achieve better performance in domain-specific use cases. Our Scale AI partnership will also help developers with expert feedback, data preparation, and support for fine-tuning and training models. 

The Statsig collaboration enables you to dynamically configure AI applications and run powerful experiments to optimize your models and applications in production. 

More RAG performance with Azure AI Search 

Retrieval-augmented generation, or RAG, is important for ensuring accurate, contextual responses and reliable information. Azure AI Search now features a generative query engine built for high performance (for select regions). Query rewriting, available in preview, transforms and creates multiple variations of a query using an SLM-trained (Small Language Model) on data typically seen in generative AI applications. In addition, semantic ranker has a new reranking model, trained with insights gathered from customer feedback and industry market trends from over a year.  

With these improvements, we’ve shattered our own performance records—our new query engine delivers up to 12.5% better relevance, and is up to 2.3 times faster than last year’s stack. Customers can already take advantage of better RAG performance today, without having to configure or customize any settings. That means improved RAG performance is delivered out of the box, with all the hard work done for you.

Effortless RAG with GitHub models and Azure AI Search—just add data 

Azure AI Search will soon power RAG in GitHub Models, offering you the same easy access glide path to bring RAG to your developer environment in GitHub Codespaces. In just a few clicks, you can experiment with RAG and your data. Directly from the playground, simply upload your data (just drag and drop), and a free Azure AI Search index will automatically be provisioned. 

Once you’re ready to build, copy/paste a code snippet into your dev environment to add more data or try out more advanced retrieval methods offered by Azure AI Search. 

This means you can unlock a full-featured knowledge retrieval system for free, without ever leaving your code. Just add data.

Advanced vector search and RAG capabilities now integrated into Azure Databases  

Vector search and RAG are transforming AI application development by enabling more intelligent, context-aware systems. Azure Databases now integrates innovations from Microsoft Research—DiskANN and GraphRAG—to provide cost-effective, scalable solutions for these technologies.

GraphRAG, available in preview in Azure Database for PostgreSQL, offers advanced RAG capabilities, enhancing large language models (LLMs) with your private PostgreSQL datasets. These integrations help empower developers, IT pros, and AI engineers alike, to build the next generation of AI applications efficiently and at cloud scale. 

DiskANN, a state-of-the-art suite of algorithms for low-latency, highly scalable vector search, is now generally available in Azure Cosmos DB and in preview for Azure Database for PostgreSQL. It’s also combined with full-text search to power Azure Cosmos DB hybrid search, currently in preview.  

Equipping you with responsible AI tooling to help ensure safety and compliance  

We continue to back up our Trustworthy AI commitments with tools you can use, and today we’re announcing two more: AI reports and risk and safety evaluations for images. These updates help ensure your AI applications are not only innovative, but safe and compliant. AI reports enable developers to document and share the use case, model card, and evaluation results for fine-tuned models and generative AI applications. Compliance teams can easily review, export, approve, and audit these reports across their organization, streamlining AI asset tracking, and governance. 

We are also excited to announce new collaborations with Credo AI and Saidot to support customers’ end-to-end AI governance. Credo AI pioneered a responsible AI platform enabling comprehensive AI governance, oversight, and accountability. Saidot’s AI Governance Platform helps enterprises and governments manage risk and compliance of their AI-powered systems with efficiency and high quality. By integrating the best of Azure AI with innovative AI governance solutions, we hope to provide our customers with choice and foster greater cross-functional collaboration to align AI solutions with their own principles and regulatory requirements.   

Transform unstructured data into multimodal app experiences with Azure AI Content Understanding  

AI capabilities are quickly advancing and expanding beyond traditional text to better reflect content and input that matches our real world. We’re introducing Azure AI Content Understanding to make it faster, easier, and more cost-effective to build multimodal applications with text, audio, images, and video. Now in preview, this service uses generative AI to extract information into customizable structured outputs.  

Pre-built templates offer a streamlined workflow and opportunities to customize outputs for a wide range of use-cases—call center analytics, marketing automation, content search, and more. And, by processing data from multiple modalities at the same time, this service can help developers reduce the complexities of building AI applications while keeping security and accuracy at the center.

Advancing the developer experience with new AI capabilities and a personal guide to Azure 

As a company of developers, we always keep the developer community top of mind with every advancement we bring to Azure. We strive to offer you the latest tech and best practices that boost impact, fit the way you work, and improve the development experience as you build AI apps. 

We’re introducing two offerings in Azure Container Apps to help transform how AI app developers work: serverless GPUs, now in preview, and dynamic sessions, available now.  

With Azure Container Apps serverless GPUs—you can seamlessly run your customer AI models on NVIDIA GPUs. This feature provides serverless scaling with optimized cold start, per-second billing, with built-in scale down to zero when not in use, and reduced operational overhead. It supports easy real-time inferencing for custom AI models, allowing you to focus on your core AI code without worrying about managing GPU infrastructure. 

Azure Container Apps dynamic sessions—offer fast access to secure sandboxed environments. These sessions are perfect for running code that requires strong isolation, such as large language model (LLM) generated code or extending and customizing software as a service (SaaS) apps. You can mitigate risks, leverage serverless scale, and reduce operational overhead in a cost-efficient manner. Dynamic sessions come with a Python code interpreter pre-installed with popular libraries, making it easy to execute common code scenarios without managing infrastructure or containers. 

These new offerings are part of our ongoing work to put Azure’s comprehensive dev capabilities within easy reach. They come right on the heels of announcing the preview of GitHub Copilot for Azure, which is like having a personal guide to Azure. By integrating with tools you already use, GitHub Copilot for Azure enhances Copilot Chat capabilities to help manage resources and deploy applications and the “@azure” command provides personalized guidance without ever leaving the code.

Updates to our intelligent data platform and Microsoft Fabric help propel AI innovation through your unique data  

While AI capabilities are remarkable, even the most powerful models don’t know your specific business. Unlocking AI’s full value requires integrating your organization’s unique data—a modern, fully integrated data estate forms the bedrock of innovation. Fast and reliable access to high-quality data becomes critical as AI applications handle increasing volumes of data requests. This is why we believe in the power of our Intelligent Data Platform as an ideal data and AI foundation for every organization’s success, today and tomorrow.   

To help meet the need for high-quality data in AI applications, we’re pleased to announce that Azure Managed Redis is now in preview. In-memory caching helps boost app performance by reducing latency and offloading traffic from databases. This new service offers up to 99.999% availability³ and comprehensive support—all while being more cost-effective than the current offering. The best part? Azure Managed Redis goes beyond standard caching to optimize AI app performance and works with Azure services. The latest Redis innovations, including advanced search capabilities and support for a variety of data types, are accessible across all service tiers_⁴.  

Just about a year ago we introduced Microsoft Fabric as our end-to-end data analytics platform that brought together all the data and analytics tools that organizations needed to empower data and business professionals alike to unlock the potential of their data and lay the foundation for the era of AI.​ Be sure to check out Arun Ulag’s blog today to learn all about the new Fabric features and integrations we’re announcing this week to help prepare your organization for the era of AI with a single, AI-powered data platform—including the introduction of Fabric Databases.  

How will you create the future? 

As AI transforms industries and unveils new opportunities, we’re committed to providing practical solutions and powerful innovation to empower you to thrive in this evolving landscape. Everything we’re delivering today reflects our dedication to meeting the real-world needs of both developers and business leaders, ensuring every person and every organization can harness the transformative power of AI.  

With these tools at your disposal, I’m excited to see how you’ll shape the future. Have a great Ignite week! 

Make the most of Ignite 2024 

• Read our Ignite announcements on how to scale your AI transformation with a powerful, secure, and adaptive cloud infrastructure.

• Tune in for can’t-miss sessions at Ignite 2024:
  • Microsoft Ignite Keynote with Satya Nadella and Microsoft leaders.
  • Empowering your AI Ambitions with Azure.
  • Azure AI unlocking the AI revolution.
  • Microsoft Fabric What’s new and what’s next.
  • Trustworthy AI Future trends and best practices.

• Do a deep dive on all the product innovation rolling out this week over on Tech Community. 

• Check out the Master GenAI Model Selection, Evaluation, and Multimodal Integration with Azure AI Foundry Plan on Microsoft Learn. With this outcome-based skilling plan, you can explore the process of selecting and applying GenAI models, how to benchmark and apply multimodal models, and complete evaluations to ensure performance and safety.

• Find out how we’re making it easy to discover, buy, deploy, and manage cloud and AI solutions via the Microsoft commercial marketplace, and get connected to vetted partner solutions today. 

• We’re here to help. Check out Azure Essentials guidance for a comprehensive framework to navigate this complex landscape, and ensure your AI initiatives not only succeed but become catalysts for innovation and growth.

References

^{1. Four futures of generative AI in the enterprise: Scenario planning for strategic resilience and adaptability.}

^{2. Microsoft Fiscal Year 2025 First Quarter Earnings Conference Call.}

^{2. Up to 99.999% uptime SLA is planned for the General Availability of Azure Managed Redis.}

^{3. B0, B1 SKU options, and Flash Optimized tier, may not have access to all features and capabilities.}

The post The next wave of Azure innovation: Azure AI Foundry, intelligent data, and more appeared first on Microsoft AI Blogs.

From agile startups to multinational corporations, Microsoft’s infrastructure offers more choice in performance, power, and cost efficiency so that our customers can continue to innovate. At Microsoft Ignite, we’re introducing significant updates across our entire cloud and AI infrastructure, from advancements in chips and liquid cooling, to new data integrations, and more flexible cloud deployments.

Unveiling the latest silicon updates across Azure infrastructure

As part of our systems approach in optimizing every layer in our infrastructure, we continue to combine the best of industry and innovate from our own unique perspectives. In addition to Azure Maia AI accelerators and Azure Cobalt central processing units (CPUs), Microsoft is expanding our custom silicon portfolio to further enhance our infrastructure to deliver more efficiency and security. Azure Integrated HSM (hardware security module) is our newest in-house security chip, which is a dedicated hardware security module that hardens key management to allow encryption and signing keys to remain within the bounds of the HSM, without compromising performance or increasing latency. Azure Integrated HSM will be installed in every new server in Microsoft’s datacenters starting next year to increase protection across Azure’s hardware fleet for both confidential and general-purpose workloads.

We are also introducing Azure Boost DPU, our first in-house DPU designed for data-centric workloads with high efficiency and low power, capable of absorbing multiple components of a traditional server into a single dedicated silicon. We expect future DPU equipped servers to run cloud storage workloads at three times less power and four times the performance compared to existing servers.

We also continue to advance our cooling technology for GPUs and AI accelerators with our next generation liquid cooling “sidekick” rack (heat exchanger unit) supporting AI systems comprised of silicon from industry leaders as well as our own. The unit can be retrofitted into Azure datacenters to support cooling of large-scale AI systems, such as ones from NVIDIA including GB200 in our AI Infrastructure.

In addition to cooling, we are optimizing how we deliver power more efficiently to meet the evolving demands of AI and hyperscale systems. We have collaborated with Meta on a new disaggregated power rack design, aimed at enhancing flexibility and scalability as we bring in AI infrastructure into our existing datacenter footprint. Each disaggregated power rack will feature 400-volt DC power that enables up to 35% more AI accelerators in each server rack, enabling dynamic power adjustments to meet the different demands of AI workloads. We are open sourcing these cooling and power rack specifications through the Open Compute Project so that the industry can benefit. 

Azure’s AI infrastructure builds on this innovation at the hardware and silicon layer to power some of the most groundbreaking AI advancements in the world, from revolutionary frontier models to large scale generative inferencing. In October, we announced the launch of the ND H200 V5 Virtual Machine (VM) series, which utilizes NVIDIA’s H200 GPUs with enhanced memory bandwidth. Our continuous software optimization efforts across these VMs means Azure delivers performance improvements generation over generation. Between NVIDIA H100 and H200 GPUs that performance improvement rate was twice that of the industry, demonstrated across industry benchmarking.

We are also excited to announce that Microsoft is bringing the NVIDIA Blackwell platform to the cloud. We are beginning to bring these systems online in preview, co-validating and co-optimizing with NIVIDIA and other AI leaders. Azure ND GB200 v6 will be a new AI optimized Virtual Machines series and combines the NVIDIA GB200 NVL 72 rack-scale design with state-of-the-art Quantum InfiniBand networking to connect tens of thousands of Blackwell GPUs to deliver AI supercomputing performance at scale. 

We are also sharing today our latest advancements in CPU-based supercomputing, the Azure HBv5 virtual machine. Powered by custom AMD EPYC^TM 9V64H processors only available on Azure, these VMs will be up to eight times faster than the latest bare-metal and cloud alternatives on a variety of HPC workloads, and up to 35 times faster than on-premises servers at the end of their lifecycle. These performance improvements are made possible by 7 TB/s of memory bandwidth from high bandwidth memory (HBM) and the most scalable AMD EPYC server platform to date. Customers can now sign up for the preview of HBv5 virtual machines, which will begin in 2025. 

Accelerating AI innovation through cloud migration and modernization 

To get the most from AI, organizations need to integrate data residing in their critical business applications. Migrating and modernizing these applications to the cloud helps enable that integration and paves the path to faster innovation while delivering improved performance and scalability. Choosing Azure means selecting a platform that natively supports all the mission-critical enterprise applications and data you need to fully leverage advanced technologies like AI. This includes your workloads on SAP, VMware, and Oracle, as well as open-source software and Linux.

For example, thousands of customers run their SAP ERP applications on Azure and we are bringing unique innovation to these organizations such as the integration between Microsoft Copilot and SAP’s AI assistant Joule. Companies like L’Oreal, Hilti, Unilever, and Zeiss have migrated their mission-critical SAP workloads to Azure so they can innovate faster. And since the launch of Azure VMware Solution, we’ve been working to support customers globally with geographic expansion. Azure VMware Solution is now available in 33 regions, with support for VMware VCF portable subscriptions. 

We are also continually improving Oracle Database@Azure to better support the mission-critical Oracle workloads of our enterprise customers. Customers like The Craneware Group and Vodafone have adopted Oracle Database@Azure to benefit from its high performance and low latency, which allows them to focus on streamlining their operations and to get access to advanced security, data governance, and AI capabilities in the Microsoft Cloud. We’re announcing today Microsoft Purview supports Oracle Database@Azure for comprehensive data governance and compliance capabilities that organizations can use to manage, secure, and track data across Oracle workloads.  

Additionally, Oracle and Microsoft plan to provide Oracle Exadata Database Service on Exascale Infrastructure in Oracle Database@Azure for hyper-elastic scaling and pay-per-use economics. Additionally, we’ve expanded the availability of Oracle Database@Azure to a total of nine regions and enhanced Microsoft Fabric integration with Open Mirroring capabilities. 

To make it easier to migrate and modernize your applications to the cloud, starting today, you can assess your application’s readiness for Azure using Azure Migrate. The new application aware method provides technical and business insights to help you migrate entire application with all dependencies as one.

Optimizing your operations with an adaptive cloud for business growth

Azure’s multicloud and hybrid approach, or adaptive cloud, integrates separate teams, distributed locations, and diverse systems into a single model for operations, security, applications, and data. This allows organizations to utilize cloud-native and AI technologies to operate across hybrid, multicloud, edge, and IoT environments. Azure Arc plays an important role in this approach by extending Azure services to any infrastructure and supporting organizations with managing their workloads and operating across different environments. Azure Arc now has over 39,000 customers across every industry, including La Liga, Coles, and The World Bank.

We’re excited to introduce Azure Local, a new, cloud-connected, hybrid infrastructure offering provisioned and managed in Azure. Azure Local brings together Azure Stack capabilities into one unified platform. Powered by Azure Arc, Azure Local can run containers, servers and Azure Virtual Desktop on Microsoft-validated hardware from Dell, HPE, Lenovo, and more. This unlocks new possibilities to meet custom latency, near real-time data processing, and compliance requirements. Azure Local comes with enhanced default security settings to protect your data and flexible configuration options, like GPU-enabled servers for AI inferencing.

We recently announced the general availability of Windows Server 2025, with new features that include easier upgrades, advanced security, and capabilities that enable AI and machine learning. Additionally, Windows Server 2025 is previewing a hotpatching subscription option enabled by Azure Arc that will allow organizations to install updates with fewer restarts—a major time saver.

We’re also announcing the preview of SQL Server 2025, an enterprise AI-ready database platform that leverages Azure Arc to deliver cloud agility anywhere. This new version continues its industry-leading security and performance and has AI built-in, simplifying AI application development and retrieval augmented generation (RAG) patterns with secure, performant, and easy-to-use vector support. With Azure Arc, SQL Server 2025 offers cloud capabilities to help customers better manage, secure, and govern SQL estate at scale across on-premises and cloud.

Transform with Azure infrastructure to achieve cloud and AI success

Successful transformation with AI starts with a powerful, secure, and adaptive infrastructure strategy. And as you evolve, you need a cloud platform that adapts and scales with your needs. Azure is that platform, providing the optimal environment for integrating your applications and data so that you can start innovating with AI. As you design, deploy, and manage your environment and workloads on Azure, you have access to best practices and industry-leading technical guidance to help you accelerate your AI adoption and achieve your business goals. 

Jumpstart your AI journey at Microsoft Ignite

Key sessions at Microsoft Ignite

• BRK214: Operate infrastructure across distributed locations with Azure Arc.
• BRK 224: Specialized silicon and hardware optimized for cloud infrastructure.
• BRK239: Application centric assessment and guided migration with Azure Migrate.

Discover more announcements at Microsoft Ignite

• Discover the next wave of Azure innovation: Azure AI Foundry, intelligent data, and more.
• Accelerate your app innovation with an AI-powered platform.

Resources for AI transformation

• Build your Azure skills with Microsoft Learn.
• Learn how to improve resiliency on Azure.
• Adopt AI best practices with Azure Essentials.

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Today, we’re announcing adapted AI models, expanding our industry capabilities and enabling organizations to address their unique needs more accurately and effectively. In collaboration with industry partner experts like Bayer, Cerence, Rockwell Automation, Saifr, Siemens Digital Industries Software, Sight Machine and more, we’re making these fine-tuned models, pre-trained using industry-specific data, available to address customers’ top use cases.

Underpinning these adapted AI models is the Microsoft Cloud, our platform for industry innovation. By integrating the Microsoft Cloud with our industry-specific capabilities and a robust ecosystem of partners, we provide a secure approach to advancing innovation across industries. This collaboration allows us to create extensive scenarios for customers globally, with embedded AI capabilities — from industry data solutions in Microsoft Fabric to AI agents in Microsoft Copilot Studio to AI models in Azure AI Studio — that enable industries to realize their full potential.

Introducing adapted AI models for industry

We’re pleased to introduce these new partner-enabled models from leading organizations that are leveraging the power of Microsoft’s Phi family of small language models (SLMs). These models will be available through the Azure AI model catalog, where customers can access a wide range of AI models to build custom AI solutions in Azure AI Studio, or directly from our partners. The models available in the Azure AI model catalog can also be used to configure agents in Microsoft Copilot Studio, a platform that allows customers to create, customize and deploy AI-powered agents, which can be applied to an industry’s top use cases to address its most pressing needs.

• Bayer, a global enterprise with core competencies in the life science fields of healthcare and agriculture, will make E.L.Y. Crop Protection available in the Azure AI model catalog. A specialized SLM, it is designed to enhance crop protection sustainable use, application, compliance and knowledge within the agriculture sector. Built on Bayer’s agricultural intelligence, and trained on thousands of real-world questions on Bayer crop protection labels, the model provides ag entities, their partners and developers a valuable tool to tailor solutions for specific food and agricultural needs. The model stands out due to its commitment to responsible AI standards, scalability to farm operations of all types and sizes and customization capabilities that allow organizations to adapt the model to regional and crop-specific requirements.
• Cerence, which creates intuitive, seamless and AI-powered user experiences for the world’s leading automakers, is enhancing its in-vehicle digital assistant technology with fine-tuned SLMs within the vehicle’s hardware. CaLLM™ Edge, an automotive-specific, embedded SLM, will be available in the Azure AI model catalog. It can be used for in-car controls, such as adjusting air conditioning systems, and scenarios that involve limited or no cloud connectivity, enabling drivers to access the rich, responsive experiences they’ve come to expect from cloud-based large language models (LLMs), no matter where they are.
• Rockwell Automation, a global leader in industrial automation and digital transformation, will provide industrial AI expertise via the Azure AI model catalog. The FT Optix Food & Beverage model brings the benefits of industry-specific capabilities to frontline workers in manufacturing, supporting asset troubleshooting in the food and beverage domain. The model provides timely recommendations, explanations and knowledge about specific manufacturing processes, machines and inputs to factory floor workers and engineers.
• Saifr, a RegTech within Fidelity Investments’ innovation incubator, Fidelity Labs, will introduce four new models in the Azure AI model catalog, empowering financial institutions to better manage regulatory compliance of broker-dealer communications and investment adviser advertising. The models can highlight potential regulatory compliance risks in text (Retail Marketing Compliance model) and images (Image Detection model); explain why something was flagged (Risk Interpretation model); and suggest alternative language that might be more compliant (Language Suggestion model). Together, these models can enhance regulatory compliance by acting as an extra set of review eyes and boost efficiency by speeding up review turnarounds and time to market.
• Siemens Digital Industries Software, which helps organizations of all sizes digitally transform using software, hardware and services from the Siemens Xcelerator business platform, is introducing a new copilot for NX X software, which leverages an adapted AI model that enables users to ask natural language questions, access detailed technical insights and streamline complex design tasks for faster and smarter product development. The copilot will provide CAD designers with AI-driven recommendations and best practices to optimize the design process within the NX X experience, helping engineers implement best practices faster to ensure expected quality from design to production. The NX X copilot will be available in the Azure Marketplace and other channels.
• Sight Machine, a leader in data-driven manufacturing and industrial AI, will release Factory Namespace Manager to the Azure AI model catalog. The model analyzes existing factory data, learns the patterns and rules behind the naming conventions and then automatically translates these data field names into standardized corporate formats. This translation makes the universe of plant data in the manufacturing enterprise AI-ready, enabling manufacturers to optimize production and energy use in plants, balance production with supply chain logistics and demand and integrate factory data with enterprise data systems for end-to-end optimization. The bottling company Swire Coca-Cola USA plans to use Factory Namespace Manager to efficiently map its extensive PLC and plant floor data into its corporate data namespace.

We also encourage innovation in the open-source ecosystem and are offering five open-source Hugging Face models that are fine-tuned for summarization and sentiment analysis of financial data.

Additionally, last month we announced new healthcare AI models in Azure AI Studio. These state-of-the-art multimodal medical imaging foundation models, created in partnership with organizations like Providence and Paige.ai, empower healthcare organizations to integrate and analyze a variety of data types, leveraging intelligence in modalities other than text in specialties like ophthalmology, pathology, radiology and cardiology.

Accelerating transformation with industry agents

Microsoft also offers AI agents that are purpose-built for industry scenarios. Available in Copilot Studio, these agents can be configured to support organizations’ industry-specific needs. For example, retailers can use the Store Operations Agent to support retail store associates and the Personalized Shopping Agent to enhance customers’ shopping experiences. Manufacturers can use the Factory Operations Agent to enhance production efficiency and reduce downtime by enabling engineers and frontline workers to quickly identify and troubleshoot issues.

All this AI innovation wouldn’t be possible without a solid data estate, because AI is only as good as the data it’s built upon. By ensuring data is accurate, accessible and well integrated, organizations can unlock deeper insights and drive more effective decision-making with AI. Microsoft Fabric, a data platform built for the era of AI, helps unify disparate data sources and prepares data for advanced analytics and AI modeling. It offers industry data solutions that address each organization’s unique needs and allows them to discover, deploy and do more with AI.

At the forefront of addressing industry needs securely

At the core of our AI strategy is a commitment to trustworthy AI. This commitment encompasses safety, security and privacy, ensuring that AI solutions are built with the highest standards of integrity and responsibility. Trustworthy AI is foundational to everything we do, from how we work with customers to the capabilities we build into our products.

At Microsoft, we combine industry AI experience, insights and capabilities with a deep understanding of customer challenges and objectives. Along with a trusted ecosystem of experienced partners, we unlock the full potential of AI for each industry and business. Our goal is not just to offer or implement AI tools but to help customers succeed by embedding AI into the very core of what each industry does.

AI transformation is here, and Microsoft is at the forefront of this revolution. As we continue to navigate this new era of innovation, it’s clear that AI will play a pivotal role in shaping the future of business across all industries and that Microsoft will continue to lead the way. To learn more about how customers in a variety of industries are transforming with AI, visit How real-world businesses are transforming with AI.

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I sat down with Teresa Tung to discuss the opportunities of proprietary data and why it is so critical to value creation with AI. Tung is a researcher whose work spans breakthrough cloud technologies, including the convergence of AI, data and computing capacity. She’s a prolific inventor, holding over 225 patents and applications. And as Accenture’s Global Lead of Data Capability, Tung leads the vision and strategy that ensures the company is prepared for ever-changing data advancements.  

We discussed a host of topics, including Teresa’s six insights.

Finally, we concluded with Teresa’s Advice for business leaders using or interested in AI 

Susan Etlinger (SE): In your recent article, “The new data essentials,” you laid out the notion that proprietary data is an organization’s competitive advantage. Would you elaborate?  

Teresa Tung (TT): Until now, data has been treated as a project. When new insights are needed, it can take months to source the data, access it, analyze it, and publish insights. If those insights spur new questions, that process must be repeated. And if the data team has bandwidth limitations or budget constraints, even more time is needed. 

“Instead of treating it as a project—an afterthought—proprietary data should be treated as a core competitive advantage.”

Generative AI models are pre-trained on an existing corpus of internet-scale data, which makes it easy to begin on day one. But they don’t know your business, people, products or processes and, without that proprietary data, models will deliver the same results to you as they do your competitors.   

Companies invest every day in products based solely on their opportunity. We know the opportunity of data and AI—improved decision making, reduced risk, new paths to monetization—so shouldn’t we think about investing in data similarly? 

SE: Since so much of a company’s proprietary knowledge sits within unstructured data, can you talk about its importance? 

TT: Yes, most businesses run on structured data—data in tabular form. But most data is unstructured. From voice messages to images to video, unstructured data is high fidelity. It captures nuance. Here’s an example: if a customer calls customer support and leaves a product review, that data could be extracted by its components and transferred to a table. But without nuanced inputs like the customer’s tone of voice or even curse words, there isn’t a complete and accurate picture of that transaction.  

Unstructured data has historically been challenging to work with, but generative AI excels at it. It actually needs unstructured data’s rich context to be trained. It’s so important in the age of generative AI. 

SE: We hear a lot about synthetic data these days. How do you think about it? 

TT: Synthetic data is necessary to fill in data gaps. It enables companies to explore multiple scenarios without the extensive costs or risks associated with real data collection.  

Advertising agencies can run various campaign images to forecast audience reactions, for example. For automotive manufacturers training self-driving cars, pushing cars into dangerous situations isn’t an option. Synthetic data teaches AI—and therefore the car—what to do in edge situations, including heavy rain or a surprise pedestrian crossing.  

Then there’s the idea of knowledge distillation. If you’re using the technique to create data with a larger language model—let’s say, a 13-billion-parameter model—that data can be used to fine tune a smaller model, making the smaller model more efficient, cost effective, or deployable to a smaller device. 

AI is so hungry. It needs representative data sets of good scenarios, edge conditions, and everything in between to be relevant. That’s the potential of synthetic data.   

SE: Unstructured data is generally data that human beings generate, so it’s often case-specific. Can you share more about why context is so important?   

TT: Context is key. We can capture it in a semantic layer or a domain knowledge graph. It’s the meaning behind the data. 

Think about every domain expert in a workplace. If a company runs a 360-degree customer data report that spans domains or even systems, one domain expert will analyze it for prospective customers, another for customer service and support, and another for customer billing. Each of these experts wants to see all the data but for their own purpose. Knowing trends within customer support may influence a marketing campaign approach, for example. 

Words often have different meanings, as well. If I say, “that’s hot for summer,” context will determine whether I was implying temperature or trend.  

Generative AI helps surface the right information at the right time to the right domain expert. 

SE: Given the pace and power of intelligent technologies, data and AI governance and security are top of mind. What trends are you noticing or forecasting? 

TT: New opportunities come with new risks. Generative AI is so easy to use, it makes everybody a data worker. That’s the opportunity and the risk. 

Because it’s easy, generative AI embedded in apps can lead to unintended data leakage. For this reason, it’s critical to think through all the implications of generative AI apps to reduce the risk that they inadvertently reveal confidential information. 

We need to rethink data governance and security. Everyone in an organization needs to be aware of the risks and of what they’re doing. We also need to think about new tooling like watermarking and confidential compute, where generative AI algorithms can be run within a secure enclave.  

SE: You’ve said generative AI can jumpstart data readiness. Can you elaborate on that? 

TT: Sure. Generative AI needs your data, but it can also help your data.  

By applying it to your existing data and processes, generative AI can build a more dynamic data supply chain, from capture and curation to consumption. It can classify and tag metadata, and it can generate design documents and deployment scripts.  

It can also support the reverse engineering of an existing system prior to migration and modernization. It’s common to think data can’t be used because it’s in an old system that isn’t yet cloud enabled. But generative AI can jumpstart the process; it can help you understand data, map relationships across data and concepts, and even write the program including the testing and documentation. 

Generative AI changes what we do with data. It can simplify and speed up the process by replacing one-off dashboards with interactivity, like a chat interface. We should spend less time wrangling data into structured formats by doing more with unstructured data.  

SE: Finally, what advice would you give to business and technology leaders who want to build competitive advantage with data? 

TT: Start now or get left behind.  

We’ve woken up to the potential AI can bring, but its potential can only be reached with your organization’s proprietary data. Without that input, your result will be the same as everyone else’s or, worse, inaccurate. 

I encourage organizations to focus on getting their digital core AI-ready. A modern digital core is the technology capability to drive data in AI-led reinvention. It’s your organization’s mix of cloud infrastructure, data and AI capabilities, and applications and platforms, with security designed into every level. Your data foundation—as part of your digital core—is essential for housing, cleansing and securing your data, ensuring it’s high quality, governed and ready for AI.  

Without a strong digital core, you don’t have the proverbial eyes to see, brain to think, or hands to act.  

Your data is your competitive differentiator in the era of generative AI. 

Teresa Tung, Ph.D. is Global Data Capability Lead at Accenture. A prolific inventor with over 225 patents, Tung specializes in bridging enterprise needs with breakthrough technologies.   

Learn more about how to get your data AI-ready: 

• Get to know the Microsoft Intelligent Data Platform. 

• Learn why a strong data and AI foundation is essential to every organization’s success, today and tomorrow.  

• Learn how to develop an intelligent data strategy that endures in the era of AI with the downloadable e-book. 
• Watch this on-demand webinar to hear Susan and Teresa go deeper on how to extract the most value from data to differentiate from competition. Learn about new ways of defining data that will help drive your AI strategy, the importance of preparing your “digital core” in advance of AI, and how to rethink data governance and security in the AI era.

Visit Azure Innovation Insights for more executive perspective and guidance on how to transform your business with cloud. 

The post 6 insights to make your data AI-ready, with Accenture’s Teresa Tung appeared first on Microsoft AI Blogs.

As a company of developers who builds for other developers, we’re excited to be part of this change and many of us will be at GitHub Universe to share our experience and learn from others about how AI is reshaping how we work. We’re not coming empty handed. I’m excited to announce new capabilities and tools that further integrate Microsoft Azure AI services directly in your favorite dev tools.

With seamless integration among Visual Studio (VS) Code, GitHub, and Azure, we provide an AI-powered, end-to-end development platform building on strong community support to help you transform your apps with AI. Read on for the details and be sure to catch up on all the GitHub news this week.

Now in preview: GitHub Copilot for Azure, your personal expert

By integrating with tools you already use, like GitHub and Visual Studio Code, GitHub Copilot for Azure builds upon the Copilot Chat capabilities in VS Code to help you manage resources and deploy applications. Using “@azure,” you can get personalized guidance to learn about services and tools without leaving your code. This can accelerate and streamline development by provisioning and deploying Azure resources through Azure Developer CLI (azd) templates. GitHub Copilot for Azure also helps you diagnose issues and answer questions about resources and costs, freeing your time to focus on whatever you prefer while GitHub Copilot for Azure takes care of the rest. Get started today.

Deploy in as little as five minutes with AI App Templates

AI App Templates accelerate your development by helping you get started faster and simplifying evaluation and the path to production. You can use AI App Templates directly in your preferred development environment such as GitHub Codespaces, VS Code, and Visual Studio. You can even get recommendations for specific templates right from GitHub Copilot for Azure based on your AI use case or scenario. Most importantly, the templates provide flexibility and choice, offering a variety of models, frameworks, programming languages, and solutions from popular AI toolchain vendors such as Arize, LangChain, LlamaIndex, and Pinecone. You can deploy full apps at once or start with app components, provisioning resources across Azure and partner services. The templates also include recommendations for added security, like using Managed Identity and keyless authentication flows. Get started.

Customize and scale your AI apps

To empower you to quickly discover, learn, and experiment with a range of the latest, most advanced AI models, GitHub announced today that GitHub Models is now in preview, bringing you Azure AI’s leading model selection direct to GitHub. Building on that theme, the Azure AI model inference API now enables you to explore and access Azure AI models directly through GitHub Marketplace. Compare model performance, experiment, and mix-and-match a variety of models, including advanced proprietary and open models that support a broad range of tasks, for free (usage limits apply).

Once you’ve selected your model and are ready to customize and deploy, you can seamlessly setup and login to your Azure account to scale from free token usage to paid endpoints with enterprise-level security and monitoring in production. Learn more.

Simplify Java Runtime updates with GitHub Copilot upgrade assistant for Java

Keeping your Java apps up to date can be a time-consuming task. GitHub Copilot upgrade assistant for Java offers an approach using AI to simplify this process and allowing you to upgrade your Java applications with minimal manual effort. Integrated into popular tools like Visual Studio Code, the GitHub Copilot upgrade assistant for Java generates an upgrade plan and guides you through a step-by-step process to transition from an older Java runtime to a newer version with optional dependencies and frameworks such as Spring Boot and JUnit. During the upgrade, the assistant automatically fixes issues through a dynamic build or fix loop and uses a human-in-the-loop approach for you to address other errors and make fixes if necessary. It ensures transparency by providing access to logs, code changes, outputs, and details at every step, giving you full control while benefiting from enhanced AI automation throughout the process. Once the upgrade is complete, you can easily review the detailed summary, and inspect all code modifications, making the entire upgrade process smooth and efficient, allowing you to focus on innovation instead of manual maintenance.

Scale AI applications with Azure AI evaluation and online A/B experimentation using CI/CD workflows 

Given trade-offs between business impact, risk and cost, you need to be able to continuously evaluate your AI applications and run A/B experiments at scale. We are significantly simplifying this process with GitHub Actions that can be integrated seamlessly into existing CI/CD workflows in GitHub. In your CI workflows, you will be able to run automated evaluation after changes are committed leveraging the Azure AI Evaluation SDK to compute metrics such as coherence and fluency. Following successful deployment, A/B experiments are automatically created and analyzed using out of the box AI model metrics and custom metrics as part of CD workflows. Along the way you can also engage with a GitHub Copilot for Azure plugin that assists with experimentation, creates metrics, powers decisions and more. Stay tuned for more details at Microsoft Ignite and sign up for our private preview to learn more! 

We trust our business with Azure, you can trust it with yours 

As you explore new AI capabilities for your organization, the platform you choose matters. Today, 95% of Fortune 500 companies trust their operations on Azure. Our business, including Microsoft 365, Dynamics 365, Bing, Copilots, etc., also runs on Azure. The same tools and services we use to build and run Microsoft are available for you. Our integration with GitHub and Visual Studio Code simplifies building with AI on Azure. And with more than 60 data center regions globally and a dedicated security team, Azure offers a reliable and secure foundation for your AI projects. All great reasons to build your next AI app with GitHub and Azure. 

And, if you’re at GitHub Universe this week, stop by and say hello to the Azure team.

About Jessica

Jessica leads data, AI, and digital application product marketing at Microsoft. Find Jessica’s blog posts here and be sure to follow Jessica on LinkedIn. 

The post Azure at GitHub Universe: New tools to help simplify AI app development appeared first on Microsoft AI Blogs.

As a company of developers who builds for other developers, we’re excited to be part of this change and many of us will be at GitHub Universe to share our experience and learn from others about how AI is reshaping how we work. We’re not coming empty handed. I’m excited to announce new capabilities and tools that further integrate Microsoft Azure AI services directly in your favorite dev tools.

With seamless integration among Visual Studio (VS) Code, GitHub, and Azure, we provide an AI-powered, end-to-end development platform building on strong community support to help you transform your apps with AI. Read on for the details and be sure to catch up on all the GitHub news this week.

Now in preview: GitHub Copilot for Azure, your personal expert

By integrating with tools you already use, like GitHub and Visual Studio Code, GitHub Copilot for Azure builds upon the Copilot Chat capabilities in VS Code to help you manage resources and deploy applications. Using “@azure,” you can get personalized guidance to learn about services and tools without leaving your code. This can accelerate and streamline development by provisioning and deploying Azure resources through Azure Developer CLI (azd) templates. GitHub Copilot for Azure also helps you diagnose issues and answer questions about resources and costs, freeing your time to focus on whatever you prefer while GitHub Copilot for Azure takes care of the rest. Get started today.

Deploy in as little as five minutes with AI App Templates

AI App Templates accelerate your development by helping you get started faster and simplifying evaluation and the path to production. You can use AI App Templates directly in your preferred development environment such as GitHub Codespaces, VS Code, and Visual Studio. You can even get recommendations for specific templates right from GitHub Copilot for Azure based on your AI use case or scenario. Most importantly, the templates provide flexibility and choice, offering a variety of models, frameworks, programming languages, and solutions from popular AI toolchain vendors such as Arize, LangChain, LlamaIndex, and Pinecone. You can deploy full apps at once or start with app components, provisioning resources across Azure and partner services. The templates also include recommendations for added security, like using Managed Identity and keyless authentication flows. Get started.

Customize and scale your AI apps

To empower you to quickly discover, learn, and experiment with a range of the latest, most advanced AI models, GitHub announced today that GitHub Models is now in preview, bringing you Azure AI’s leading model selection direct to GitHub. Building on that theme, the Azure AI model inference API now enables you to explore and access Azure AI models directly through GitHub Marketplace. Compare model performance, experiment, and mix-and-match a variety of models, including advanced proprietary and open models that support a broad range of tasks, for free (usage limits apply).

Once you’ve selected your model and are ready to customize and deploy, you can seamlessly setup and login to your Azure account to scale from free token usage to paid endpoints with enterprise-level security and monitoring in production. Learn more.

Simplify Java Runtime updates with GitHub Copilot upgrade assistant for Java

Keeping your Java apps up to date can be a time-consuming task. GitHub Copilot upgrade assistant for Java offers an approach using AI to simplify this process and allowing you to upgrade your Java applications with minimal manual effort. Integrated into popular tools like Visual Studio Code, the GitHub Copilot upgrade assistant for Java generates an upgrade plan and guides you through a step-by-step process to transition from an older Java runtime to a newer version with optional dependencies and frameworks such as Spring Boot and JUnit. During the upgrade, the assistant automatically fixes issues through a dynamic build or fix loop and uses a human-in-the-loop approach for you to address other errors and make fixes if necessary. It ensures transparency by providing access to logs, code changes, outputs, and details at every step, giving you full control while benefiting from enhanced AI automation throughout the process. Once the upgrade is complete, you can easily review the detailed summary, and inspect all code modifications, making the entire upgrade process smooth and efficient, allowing you to focus on innovation instead of manual maintenance.

Scale AI applications with Azure AI evaluation and online A/B experimentation using CI/CD workflows 

Given trade-offs between business impact, risk and cost, you need to be able to continuously evaluate your AI applications and run A/B experiments at scale. We are significantly simplifying this process with GitHub Actions that can be integrated seamlessly into existing CI/CD workflows in GitHub. In your CI workflows, you will be able to run automated evaluation after changes are committed leveraging the Azure AI Evaluation SDK to compute metrics such as coherence and fluency. Following successful deployment, A/B experiments are automatically created and analyzed using out of the box AI model metrics and custom metrics as part of CD workflows. Along the way you can also engage with a GitHub Copilot for Azure plugin that assists with experimentation, creates metrics, powers decisions and more. Stay tuned for more details at Microsoft Ignite and sign up for our private preview to learn more! 

We trust our business with Azure, you can trust it with yours 

As you explore new AI capabilities for your organization, the platform you choose matters. Today, 95% of Fortune 500 companies trust their operations on Azure. Our business, including Microsoft 365, Dynamics 365, Bing, Copilots, etc., also runs on Azure. The same tools and services we use to build and run Microsoft are available for you. Our integration with GitHub and Visual Studio Code simplifies building with AI on Azure. And with more than 60 data center regions globally and a dedicated security team, Azure offers a reliable and secure foundation for your AI projects. All great reasons to build your next AI app with GitHub and Azure. 

And, if you’re at GitHub Universe this week, stop by and say hello to the Azure team.

About Jessica

Jessica leads data, AI, and digital application product marketing at Microsoft. Find Jessica’s blog posts here and be sure to follow Jessica on LinkedIn. 

The post Azure at GitHub Universe: New tools to help simplify AI app development appeared first on Microsoft AI Blogs.

With the launch of Azure OpenAI Service Data Zones in the European Union and the United States, enterprises can now scale their AI workloads with even greater ease while maintaining compliance with regional data residency requirements. Historically, variances in model-region availability forced customers to manage multiple resources, often slowing down development and complicating operations. Azure OpenAI Service Data Zones can remove that friction by offering flexible, multi-regional data processing while ensuring data is processed and stored within the selected data boundary.

This is a compliance win which also allows businesses to seamlessly scale their AI operations across regions, optimizing for both performance and reliability without having to navigate the complexities of managing traffic across disparate systems.

Leya, a tech startup building genAI platform for legal professionals, has been exploring Data Zones deployment option.

“Azure OpenAI Service Data Zones deployment option offers Leya a cost-efficient way to securely scale AI applications to thousands of lawyers, ensuring compliance and top performance. It helps us achieve better customer quality and control, with rapid access to the latest Azure OpenAI innovations.“—Sigge Labor, CTO, Leya

Data Zones will be available for both Standard (PayGo) and Provisioned offerings, starting this week on November 1, 2024.

Industry leading performance

Enterprises depend on predictability, especially when deploying mission-critical applications. That’s why we’re introducing a 99% latency service level agreement for token generation. This latency SLA ensures that tokens are generated at a faster and more consistent speeds, especially at high volumes

The Provisioned offer provides predictable performance for your application. Whether you’re in e-commerce, healthcare, or financial services, the ability to depend on low-latency and high-reliability AI infrastructure translates directly to better customer experiences and more efficient operations.

Lowering the cost of getting started

To make it easier to test, scale, and manage, we are reducing hourly pricing for Provisioned Global and Provisioned Data Zone deployments starting November 1, 2024. This reduction in cost ensures that our customers can benefit from these new features without the burden of high expenses. Provisioned offering continues to offer discounts for monthly and annual commitments.

We are also reducing deployment minimum entry points for Provisioned Global deployment by 70% and scaling increments by up to 90%, lowering the barrier for businesses to get started with Provisioned offering earlier in their development lifecycle.

Deployment quantity minimums and increments for Provisioned offering

For developers and IT teams, this means faster time-to-deployment and less friction when transitioning from Standard to Provisioned offering. As businesses grow, these simple transitions become vital to maintaining agility while scaling AI applications globally.

Efficiency through caching: A game-changer for high-volume applications

Another new feature is Prompt Caching, which offers cheaper and faster inference for repetitive API requests. Cached tokens are 50% off for Standard. For applications that frequently send the same system prompts and instructions, this improvement provides a significant cost and performance advantage.

By caching prompts, organizations can maximize their throughput without needing to reprocess identical requests repeatedly, all while reducing costs. This is particularly beneficial for high-traffic environments, where even slight performance boosts can translate into tangible business gains.

A new era of model flexibility and performance

One of the key benefits of the Provisioned offering is that it is flexible, with one simple hourly, monthly, and yearly price that applies to all available models. We’ve also heard your feedback that it is difficult to understand how many tokens per minute (TPM) you get for each model on Provisioned deployments. We now provide a simplified view of the number of input and output tokens per minute for each Provisioned deployment. Customers no longer need to rely on detailed conversion tables or calculators. 

We are maintaining the flexibility that customers love with the Provisioned offering. With monthly and annual commitments you can still change the model and version—like GPT-4o and GPT-4o-mini—within the reservation period without losing any discount. This agility allows businesses to experiment, iterate, and evolve their AI deployments without incurring unnecessary costs or having to restructure their infrastructure.

Enterprise readiness in action

Azure OpenAI’s continuous innovations aren’t just theoretical; they’re already delivering results in various industries. For instance, companies like AT&T, H&R Block, Mercedes, and more are using Azure OpenAI Service not just as a tool, but as a transformational asset that reshapes how they operate and engage with customers.

Beyond models: The enterprise-grade promise

It’s clear that the future of AI is about much more than just offering the latest models. While powerful models like GPT-4o and GPT-4o-mini provide the foundation, it’s the supporting infrastructure—such as Provisioned offering, Data Zones deployment option, SLAs, caching, and simplified deployment flows—that truly make Azure OpenAI Service enterprise-ready.

Microsoft’s vision is to provide not only cutting-edge AI models but also the enterprise-grade tools and support that allow businesses to scale these models confidently, securely, and cost-effectively. From enabling low-latency, high-reliability deployments to offering flexible and simplified infrastructure, Azure OpenAI Service empowers enterprises to fully embrace the future of AI-driven innovation.

Get started today

As the AI landscape continues to evolve, the need for scalable, flexible, and reliable AI solutions becomes even more critical for enterprise success. With the latest enhancements to Azure OpenAI Service, Microsoft is delivering on that promise—giving customers not just access to world-class AI models, but the tools and infrastructure to operationalize them at scale.

Now is the time for businesses to unlock the full potential of generative AI with Azure, moving beyond experimentation into real-world, enterprise-grade applications that drive measurable outcomes. Whether you’re scaling a virtual assistant, developing real-time voice applications, or transforming customer service with AI, Azure OpenAI Service provides the enterprise-ready platform you need to innovate and grow.

The post Accelerate scale with Azure OpenAI Service Provisioned offering appeared first on Microsoft AI Blogs.

Microsoft offers a comprehensive platform with industry-leading AI and cloud services for organizations to innovate with confidence. Microsoft Azure provides a full range of Infrastructure as a Service (IaaS) and Platform as a Service (PaaS) solutions that cater to every enterprise IT scenario. Azure’s strong adaptive cloud functionality allows businesses to seamlessly integrate their on-premises, multicloud, and edge environments with the cloud.

AI is reenergizing the promise of technology and transforming industries faster than we thought possible. Being ready to transform with AI means that businesses need to have a clear strategic vision, invest in data infrastructure, foster cross-functional collaboration, upskill their workforce, and integrate advanced technologies into their core operations to drive efficiency. Azure offers the flexibility required, whether it’s operating across distributed environments from hybrid, multicloud, and edge, or deploying various workloads and data sources tailored to their business needs. As our customers grow their businesses, they want a trusted, industry-leading partner to safeguard their data, ensure compliance, help modernize their data estate and infrastructure, and enable them to build intelligent applications that meet the demands of substantial data processing and computing power.

A cloud platform that supports your innovations

Azure’s strength as a platform lies in its ability to advance your organization with industry-leading AI and cloud services; it is refined in every layer from silicon to software and custom designed to deliver the performance you need for AI and any type of workloads. And no matter the use case, Azure AI has you covered. We have the broadest selection of models—from Microsoft’s small language models, Phi-3 family, to frontier, and open models including OpenAI and models from Meta, Mistral, and Cohere.

Microsoft has the expertise and scale to run the AI supercomputers that power some of the world’s biggest AI services, such as Microsoft Azure OpenAI Service, ChatGPT, Bing, and more. Our end-to-end systems innovation leveraging the best silicon across the industry from partners such as Nvidia, AMD, and Intel as well as our in-house Maia and Cobalt, to innovations such as Hollow Core Fiber and Azure Boost, optimizes at every layer for performance and efficiency, while supporting all applications and data that integrate with the AI models you use.

We now have more than 60,000 customers using Azure AI and seeing the benefits of automation, personal productivity, customer engagement, and the creation of net new products. Our platform also enables large-scale generative AI experiences with built-in safety and security. By providing developers with a comprehensive set of cloud-based AI services and tools, Azure AI allows them to build intelligent applications without requiring specialized AI or data science expertise.

For example, Mercedes-Benz aimed to provide a highly personalized experience for customers that aligns with their habits, predicts their needs, and most importantly, ensures drivers keep their attention on the road. Powered by Azure OpenAI Service, the company’s MBUX in-car voice assistant offers a unique ability to understand voice commands and facilitate interactive conversations. Unlike regular voice assistants that need exact commands, GPT-4 delivered through Azure OpenAI can handle complex follow-up questions and keep track of context.

Your choice of environment, workloads, and technology

Customers want the choice to transform in ways that best suit their business. Whether it’s leveraging the best of open source, operating across the globe, or running workloads and processing data across hybrid, multicloud, and edge environments, Azure provides the flexibility to do so.

With more than 300 datacenters across 34 countries, Microsoft’s infrastructure brings applications closer to users, ensures data residency, and offers robust compliance and resiliency options for our customers. We are consistently investing in expanding our global capacity to meet customers’ growing needs. This extensive network, combined with our holistic strategy, provides our customers the flexibility to achieve optimal performance, efficiency, and cost-effectiveness regardless of their location, the workloads they manage, or the open-source technologies—including Linux—that they employ.

It’s important that organizations have the choice to use the technology best suited to their business and that their developers can thrive using the open-source software of their choice. Azure offers top-tier support for a wide range of technologies, whether you’re focusing on different operating systems such as Linux, languages like Java, Python, PHP, and Ruby, databases including PostgreSQL, MySQL, or MongoDB, cloud-native services with Kubernetes, or even frameworks such as Hadoop or WordPress. SWISS International Air Lines recognized that migrating to Azure would boost flexibility, add volume, and lower costs while allowing them to use Java, Oracle, MySQL, or .NET.

Beyond open source, our deep partnerships with other enterprise market leaders such as SAP, VMware, Oracle, Databricks, Citrix, and NetApp give our customers flexibility to run mission critical enterprise workloads on Azure with optimal performance and security.

For the second consecutive year, Microsoft is also named a Leader in the new 2024 Gartner® Magic Quadrant™ for Distributed Hybrid Infrastructure. With Microsoft’s adaptive cloud approach, you can streamline your operations, increase scalability, and reduce costs while maintaining flexibility and control over your cloud environments. 

Our commitment to leading responsibly as a cloud provider

Choosing a cloud platform to boost your innovation isn’t just about having a robust AI-driven system. It’s crucial to make sure the platform is fundamentally secure. Microsoft is dedicated to Trustworthy AI and developing industry leading technology to support it. Our commitments and capabilities work together to protect our customers and developers at all levels.

At Microsoft, trust is paramount, and our expanded Secure Future Initiative (SFI) highlights our dedication to security, safety, and privacy. We recently shared our first SFI Progress Report, covering updates in culture, governance, technology, and operations. Guided by the principles of secure by design, secure by default, and secure operations, this report reaffirms our commitment to security. We have significantly invested in governance structures, policies, tools, and processes to safely develop and deploy AI. 

Our customers keep benefiting from this partnership and our Responsible AI principles. SOPHiA GENETICS, a Swiss company offering data-driven healthcare solutions, developed a cloud-based platform using AI to analyze genomic data. Given the sensitivity of this data, security was critical to their mission. By partnering with Microsoft for security solutions like Microsoft 365 E5 and Microsoft Sentinel, SOPHiA GENETICS identified and minimized their security risks, enabling them to stay focused on delivering healthcare solutions through their SOPHiA AI platform.

Accelerate your innovation and maximize cloud value 

At Microsoft, we are committed to our customers’ success and ensuring they have the support they need throughout their cloud journey. We’ve accelerated innovation with purpose-built and AI-driven industry-specific solutions so you can realize value with Microsoft Azure faster. We’ve also built a trusted ecosystem of partners who are always innovating, so you can easily keep up with industry changes. With Azure’s seamless integration of applications and solutions pre-configured for your industry, you can trust that you’ll stay ahead in a competitive market.

Start innovating today

• Achieve cloud and AI success with Azure Essentials.
• Read the 2024 Gartner® Magic Quadrant™ for Strategic Cloud Platform Services (SCPS).

Gartner, Magic Quadrant for Strategic Cloud Platform Services, David Wright, Dennis Smith, 21 October 2024,

GARTNER is a registered trademark and service mark of Gartner and Magic Quadrant  is a registered trademark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and are used herein with permission. All rights reserved. This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from Microsoft. 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

The post Microsoft named a Leader in 2024 Gartner® Magic Quadrant™ for Strategic Cloud Platform Services appeared first on Microsoft AI Blogs.

When businesses infuse cutting-edge innovations like AI into their operations, it can drive sustainable, long-term growth, futureproof them against economic headwinds, and create lasting competitive advantage. While the market dialog is dominated by incredible new AI-driven services, there is untapped value in the hundreds of millions of existing applications that can now be modernized and infused with AI.

Without modernization, organizations may miss out on the full value of their investments, lag behind the competition, or fall prey to costly disruptions—and they certainly won’t be positioned to develop tomorrow’s leading AI innovations. Pitfalls like these are already a reality for many: according to the Forrester study exploring modernization, one in four business decision-makers experienced digital platform failures due to modernization challenges.

Because true modernization can (and should) touch every area of your business, it gets complex, involving everything from processes and systems to orchestration and strategic planning. As a result, many leaders aren’t sure where to begin—which is why we’ve created this blog series to chart a course through modernization with AI readiness in mind. 

In this first blog, I’ll cover what modernization really means, why it matters for your business, and how to think about modernization as your path to AI value.

Modernization is a holistic approach 

The first step on your modernization journey is to get clear about what differentiates modernization from more piecemeal approaches. Modernization means updating and improving assets across all business areas so they work well with evolving digital software and the cloud. This is a holistic approach that includes people, processes, and skillsets along with data, apps, and infrastructure.

Modernization breathes new life into legacy technologies to prime them for AI

Earlier I mentioned that modernization is critical to the future of your business—let’s give a little background as to why. For decades, businesses grew up alongside purpose-built digital solutions that met the needs of the day. These solutions are now struggling to meet current needs. For example, they can’t easily incorporate the latest AI capabilities because they weren’t built for fast-paced innovation cycles. This tech also often works in siloes and may not be able to process large amounts of data to support the latest intelligent services. Take it from Sapiens, an insurance platform provider across thirty countries: they struggled to innovate because their digital practices were established before they adopted the cloud. 

Modernization helped Sapiens overcome these issues and prime their operations for AI innovation by migrating, transforming, and distributing their key applications. This allowed them to accelerate their development processes and their innovation cycle, since developers could devote more time and resources to improvements rather than maintenance.

“Using and investing in Microsoft Azure tools to automate some of our infrastructure and processes, we managed to cut our time to market in half and reduced our operational overhead by at least 40 percent.”—Michael Mirel, Head of Cloud and DevOps Center of Excellence at Sapiens.

They’re not alone—according to an IDC survey on the benefits of cloud migration and modernization, 41% of organizations cite operational efficiency and 30% cite cost savings as the top outcomes achieved. When businesses take this technology posture, they lay the groundwork for AI and analytics innovation and more agile operations. 

Modernization starts with the cloud, but it doesn’t end there 

Migrating to the cloud is essential (30% of respondents in that same IDC survey reported the cloud eased modernization), but think of the cloud as just one aspect of driving scale, achieving long-term outcomes, and unlocking the potential of technologies like AI. 

Take Scandinavian Airline Systems (SAS) as a case study. They incorporated the cloud as part of SAS Forward, a broad strategy to help adapt to changing market dynamics and cost pressures.

“The airline industry is highly competitive, to continue to provide great experiences for travelers, we needed to make big changes.”—Mikael Perhult, Tech Lead, Cloud at SAS – Scandinavian Airlines.

That’s why SAS Forward went beyond cloud migration, modernizing SAS’s databases and apps as a fully managed platform service on Azure.

“We wanted to transform the technologies that support and connect SAS’ systems and services for greater scalability, efficiency, and security, while paving the way for innovation for our customers.”—Prakash Ujjwal, Senior Systems Specialist, IT Infrastructure Services. 

Just as SAS went beyond initial migration, they also had to go beyond the tech itself. This meant a thoughtful reimagining of how their operations could be modernized to maximize investment.

Modernization reshapes more than technology 

While apps and platforms are essential to modernization, businesses should also align their people, processes, and skillsets to ensure the entire enterprise is working toward the same goal. This is especially true with AI, since it creates a new way of working that disrupts long-standing routines. In other words, AI is both a catalyst for modernization and accelerator of modernization. 

The research behind the Forrester Application Modernization Checklist supports this idea. One in five decision-makers reported achieving modernization, and those who succeeded said they did so because they looked at the task holistically. They built a clear strategy around AI and AI training, tied AI closely to business outcomes, and used cross-org metrics to track and measure progress and impact. And crucially, they drew on support from their strategic partners to upgrade their technology stacks—modernization is too important and too complex to tackle alone. SAS embodied this holistic approach by setting goals to transform operations and maintenance, increase overall innovation, and leverage partners to get more out of their initial cloud investments. They succeeded and streamlined developer workflows to enable continuous improvement while modernizing airline operations at scale.

The right modernization approach unlocks competitive advantage 

When businesses take a truly holistic approach to modernization, transformative outcomes speak for themselves. Sapiens reports that they’re able to extract deeper insights more easily and drive more effective business decisions, while SAS built an environment that fosters long-term innovation and has improved customer experiences.

I invite you to continue charting a modernization course with me in the next blog in our series. We’ll walk through specific steps for effective modernization identified by the Forrester Consulting Application Modernization Checklist that Microsoft commissioned.

• Download the 2024 commissioned Forrester Consulting study: Unlock Competitive Advantage with Application Modernization. 

• Read the accompanying Application Modernization Checklist. 

• Learn about the benefits of cloud migration and modernization from IDC. 

• Consult the Microsoft Application and Data Modernization: Azure page. 

• Visit the Microsoft Azure Innovate site. 

The post Achieving AI readiness through comprehensive modernization appeared first on Microsoft AI Blogs.

Now, GPT-4o-Realtime-Preview pushes the boundaries even further by integrating language generation with seamless voice interaction, giving developers the tools they need to craft more natural and conversational AI experiences. From creating virtual assistants to powering real-time customer support, this new model opens a vast array of possibilities for voice-driven applications. The new model is also integrated with Copilot, as part of the new Copilot Voice product announced.

Building on recent Azure OpenAI announcements 

This announcement continues a series of significant updates within Azure OpenAI Service, including: 

• O1 Series: A new lineup of models designed for advanced reasoning over complex data. We are happy to make the API available to our developers on Azure today after a two-week preview in the Azure AI Studio Playground. 

• Data zones: Enabling regional data residency to support customer privacy and compliance. 

• Expanded provisioned deployments: Extending availability to a global SKU for customers needing dedicated capacity. 

• General availability of fine-tuning: Allowing GPT-4o and mini models to be tailored for specialized use cases. 

• Trustworthy AI: New tooling, including evaluations in Azure AI Studio to support proactive risk assessments, and watermarking on images generated by DALL*E. 

• Cache Prompting (coming soon): Cheaper and faster inferencing through caching on GPT-4o and o1 models. 

This continuous evolution demonstrates Azure’s commitment to providing the most comprehensive, secure, and versatile AI tools to customers worldwide. Bookmark our newsfeed to track all future announcements.

What’s new in GPT-4o-Realtime-Preview? 

GPT-4o-Realtime API: With this release, GPT-4o evolves to support audio input and output, enabling real-time, natural voice-based interactions that go beyond traditional text-based AI conversations. This multimodal capability empowers developers to build innovative voice applications with ease. 

Azure AI Studio Early Access playground: For developers eager to explore, this dedicated space allows early experimentation with GPT-4o-Realtime API for Audio capabilities. The studio provides an environment to test, fine-tune, and optimize voice interactions before launching them into production environments.

Performance that speaks for itself 

Early customers using GPT-4o-Realtime API for Audio shared remarkable results, confirming its performance and impact: 

• Faster responses: GPT-4o-Realtime API for Audio provides voice responses significantly faster than many traditional text-to-speech engines, leading to reduced latency and smoother interactions. 

• Natural conversations: The model minimizes the robotic tone often associated with AI-generated speech, making conversations sound more engaging. 

• Multilingual support: The API supports a wide range of languages, allowing for natural, multilingual conversations that can be applied to global-facing applications. 

Applications of GPT-4o-Realtime-Preview in Azure OpenAI Service 

The potential of GPT-4o-Realtime-Preview spans across various industries, transforming how businesses operate and how users interact with technology: 

• Customer service: Voice-based chatbots and virtual assistants can now handle customer inquiries more naturally and efficiently, reducing wait times and improving overall satisfaction. 

• Content creation: Media producers can revolutionize their workflows by leveraging speech generation for use in video games, podcasts, and film studios. 

• Real-time translation: Industries such as healthcare and legal services can benefit from real-time audio translation, breaking down language barriers and fostering better communication in critical contexts. 

Use cases driving innovation 

The versatility of GPT-4o-Realtime-Preview is already transforming operations across a variety of sectors. Here are a few early adopters and how they’re benefiting from this technology: 

• Bosch (Germany): Integrating GPT-4o-Realtime API for Audio for virtual reality training in automotive settings, allowing consumers and technicians to receive voice-guided instructions.

“AOAI is an ideal interface for our HeyBosch – Virtual Sales Executive Solution as it is a conversation first solution. We can easily integrate AOAI to our existing solution – Thanks for the reference samples. The response time from the virtual agent has improved substantially as we now have a single interface coupling both (speech and LLM). This helps in keeping latency minimal.  This integration shows the art of possibility of creating compelling user experiences combining GenAI, 3D tech and real time speech processing capabilities.”—Vamsidhar Sunkari Senior Expert Bosch Global Software Technologies Pvt Ltd. 

• Lyrebird Health (Australia): Using GPT-4o-Realtime-Preview as a medical copilot, summarizing patient information and automating follow-up tasks in real-time.

“Lyrebird Health is excited to bring audio capabilities to the provider/patient relationship. The new GPT-4o-realtime-preview model will allow us to experiment and launch new experiences for our customers and end users. This will help us on our mission to provide the best people technology on the planet.”—Kai Van Lieshout, Co-founder and CEO of Lyrebird Health

• Azure AI Search: VoiceRAG leverages Azure OpenAI’s GPT-4o real-time audio model and Azure AI Search to create an advanced voice-based generative AI application with Retrieval-Augmented Generation (RAG). The system integrates real-time audio streaming and function calling to perform knowledge base searches, ensuring responses are well-grounded without compromising latency. By securely handling model configurations and retrieval processes on the backend, VoiceRAG provides a natural, conversational interface that includes citations seamlessly displayed in the user experience. Deep dive the VoiceRAG experience in a dedicated blog on Microsoft Tech Community.

Our commitment to Trustworthy AI 

Azure remains steadfast in its commitment to responsible AI, with safety and privacy as default priorities. The Realtime API utilizes multiple layers of safety measures, including automated monitoring and human review, to prevent misuse.

The Realtime API has undergone rigorous evaluations guided by our commitments to Responsible AI. Check out the 2024 Responsible AI Transparency Report.

Azure OpenAI Service provides built-in Content Safety features at no extra cost, and Azure AI Studio offers tools to assess the safety of your AI applications, ensuring a secure and responsible AI experience.

What’s next with GPT-4o-Realtime API for Audio?

As we continue to innovate and expand the capabilities of GPT-4o-Realtime API for Audio, we are excited to see how developers and businesses will leverage this cutting-edge technology to create voice-driven applications that push the boundaries of what’s possible. 

Whether you’re looking to integrate voice capabilities into your customer service operations or explore the possibilities of multilingual interactions, GPT-4o-Realtime API for Audio provides the flexibility and power to transform your AI solutions. Starting today, you can explore these new capabilities in the Azure OpenAI Studio, experiment with them in the Early Access Playground, or directly integrate the realtime API in public preview into your applications. 

Be sure to review our documentation for the latest updates, dive into the available use cases, and start building with GPT-4o-Realtime API for Audio to bring your business to the next level of AI innovation. 

Stay tuned for upcoming customer stories, detailed use case demos, and more as we continue to roll out updates in the weeks ahead! 

The post Announcing new products and features for Azure OpenAI Service including GPT-4o-Realtime-Preview with audio and speech capabilities appeared first on Microsoft AI Blogs.