Kategorie: Microsoft Azure

Democratizing FinOps: Transform your practice with FOCUS and Microsoft Fabric

Cloud computing has revolutionized the way you build, deploy, and scale applications and services. While you have unprecedented flexibility, agility, and scalability, you also face greater challenges in managing cost, security, and compliance. While IT security and compliance are often managed by central teams, cost is a shared responsibility across executive, finance, product, and engineering teams, which is what makes managing cloud cost such a challenge. Having the right tools to enable cross-group collaboration and make data-driven decisions is critical.

Fortunately, you have everything you need in the Microsoft Cloud to implement a streamlined FinOps practice that brings people together and connects them to the data they need to make business decisions. And with new developments like Copilot in Microsoft Cost Management and Microsoft Fabric, there couldn’t be a better time to take a fresh look at how you manage cost within your organization and how you can leverage the FinOps Framework and the FinOps Open Cost and Usage Specification (FOCUS) to accelerate your FinOps efforts.

There’s a lot to cover in this space, so I’ll split this across a series of blog posts. In this first blog post, I’ll introduce the core elements of Cost Management and Fabric that you’ll need to lay the foundation for the rest of the series, including how to export data, how FOCUS can help, and a few quick options that anyone can use to setup reports and alerts in Fabric with just a few clicks.

No-code extensibility with Cost Management exports

As your FinOps team grows to cover new services, endpoints, and datasets, you may find they spend more time integrating disparate APIs and schemas than driving business goals. This complexity also keeps simple reports and alerts just out of reach from executive, finance, and product teams. And when your stakeholders can’t get the answers they need, they push more work on to engineering teams to fill those gaps, which again, takes away from driving business goals.

We envision a future where FinOps teams can empower all stakeholders to stay informed and get the answers they need through turn-key integration and AI-assisted tooling on top of structured guidance and open specifications. And this all starts with Cost Management exports—a no-code extensibility feature that brings data to you.

As of today, you can sign up for a limited preview of Cost Management expands where you can export five new datasets directly into your storage account without a single line of code. In addition to the actual and amortized cost and usage details you get today, you’ll also see:

Cost and usage details aligned to FOCUS

Price sheets

Reservation details

Reservation recommendations

Reservation transactions

Of note, the FOCUS dataset includes both actual and amortized costs in a single dataset, which can drive additional efficiencies in your data ingestion process. You’ll benefit from reduced data processing times and more timely reporting on top of reduced storage and compute costs due to fewer rows and less duplication of data.

Beyond the new datasets, you’ll also discover optimizations that deliver large datasets more efficiently, reduced storage costs by updating rather than creating new files each day, and more. All exports are scheduled at the same time, to ensure scheduled refreshes of your reports will stay in sync with the latest data. Coupled with file partitioning, which is already available and recommended today, and data compression, which you’ll see in the coming months, the exports preview removes the need to write complex code to extract, transfer, and load large datasets reliably via APIs. This better enables all FinOps stakeholders to build custom reports to get the answers they need without having to learn a single API or write a single line of code.

To learn about all the benefits of the exports preview—yes, there’s more—read the full synopsis in Cost Management updates. And to start exporting your FOCUS cost and usage, price sheet, and reservation data, sign up for the exports preview today.

FOCUS democratizes cloud cost analytics

In case you’re not familiar, FOCUS is a groundbreaking initiative to establish a common provider and service-agnostic format for billing data that empowers organizations to better understand cost and usage patterns and optimize spending and performance across multiple cloud, software as a service (SaaS), and even on-premises service offerings. FOCUS provides a consistent, clear, and accessible view of cost data, explicitly designed for FinOps needs. As the new “language” of FinOps, FOCUS enables practitioners to collaborate more efficiently and effectively with peers throughout the organization and even maximize transferability and onboarding for new team members, getting people up and running quicker.

FOCUS 0.5 was originally announced in June 2023, and we’re excited to be leading the industry with our announcement of native support for the FOCUS 1.0 preview as part of Cost Management exports on November 13, 2023. We believe FOCUS is an important step forward for our industry, and we look forward to our industry partners joining us and collaboratively evolving the specification alongside FinOps practitioners from our collective customers and partners.

FOCUS 1.0 preview adds new columns for pricing, discounts, resources, and usage along with prescribed behaviors around how discounts are applied. Soon, you’ll also have a powerful new use case library, which offers a rich set of problems and prebuilt queries to help you get the answers you need without the guesswork. Armed with FOCUS and the FinOps Framework, you have a literal playbook on how to understand and extract answers out of your data effortlessly, enabling you to empower FinOps stakeholders regardless of how much knowledge or experience they have, to get the answers they need to maximize business value with the Microsoft Cloud.

For more details about FOCUS or why we believe it’s important, see FOCUS: A new specification for cloud cost transparency. And stay tuned for more updates as we dig into different scenarios where FOCUS can help you.

Microsoft Fabric and Copilot enable self-service analytics

So far, I’ve talked about how you can leverage Cost Management exports as a turn-key solution to extract critical details about your costs, prices, and reservations using FOCUS as a consistent, open billing data format with its use case library that is a veritable treasure map for finding answers to your FinOps questions. While these are all amazing tools that will accelerate your FinOps efforts, the true power of democratizing FinOps lies at the intersection of Cost Management and FOCUS with a platform that enables you to provide your stakeholders with self-serve analytics and alerts. And this is exactly what Microsoft Fabric brings to the picture.

Microsoft Fabric is an all-in-one analytics solution that encompasses data ingestion, normalization, cleansing, analysis, reporting, alerting, and more. I could write a separate blog post about how to implement each FinOps capability in Microsoft Fabric, but to get you acclimated, let me introduce the basics.

Your first step to leveraging Microsoft Fabric starts in Cost Management, which has done much of the work for you by exporting details about your prices, reservations, and cost and usage data aligned to FOCUS.

Once exported, you’ll ingest your data into a Fabric lakehouse, SQL, or KQL database table and create a semantic model to bring data together for any reports and alerts you’ll want to create. The database option you use will depend on how much data you have and your reporting needs. Below is an example using a KQL database, which uses Azure Data Explorer under the covers, to take advantage of the performance and scale benefits as well as the powerful query language.

Fabric offers several ways to quickly explore data from a semantic model. You can explore data by simply selecting the columns you want to see, but I recommend trying the auto-create a report option which takes that one step further by generating a quick summary based on the columns you select. As an example, here’s an auto-generated summary of the FOCUS EffectiveCost broken down by ChargePeriodStart, ServiceCategory, SubAccountName, Region, PricingCategory, and CommitmentDiscountType. You can apply quick tweaks to any visual or switch to the full edit experience to take it even further.

Those with a keen eye may notice the Copilot button at the top right. If we switch to edit mode, we can take full advantage of Copilot and even ask it to create the same summary:

Copilot starts to get a little fancier with the visuals and offers summarized numbers and a helpful filter. I can also go further with more specific questions about commitment-based discounts:

Of course, this is barely scratching the surface. With a richer semantic model including relationships and additional details, Copilot can go even further and save you time by giving you the answers you need and building reports with less time and hassle.

In addition to having unparalleled flexibility in reporting on the data in the way you want, you can also create fine-grained alerts in a more flexible way than ever before with very little effort. Simply select the visual you want to measure and specify when and how you want to be alerted:

This gets even more powerful when you add custom visuals, measures, and materialized views that offer deeper insights.

This is just a glimpse of what you can do with Cost Management and Microsoft Fabric together. I haven’t even touched on the data flows, machine learning capabilities, and the potential of ingesting data from multiple cloud providers or SaaS vendors also using FOCUS to give you a full, single pane of glass for your FinOps efforts. You can imagine the possibilities of how Copilot and Fabric can impact every FinOps capability, especially when paired with rich collaboration and automation tools like Microsoft Teams, Power Automate, and Power Apps that can help every stakeholder accomplish more together. I’ll share more about these in a future blog post or tutorial.

Next steps to accomplish your FinOps goals

I hope you’re as excited as I am about the potential of low- or even no-code solutions that empower every FinOps stakeholder with self-serve analytics. Whether you’re in finance seeking answers to complex questions that require transforming, cleansing, and joining multiple datasets, in engineering looking for a solution for near-real-time alerts and analytics that can react quickly to unexpected changes, or a FinOps team that now has more time to pursue something like unit cost economics to measure the true value of the cloud, the possibilities are endless. As someone who uses Copilot often, I can say that the potential of AI is real. Copilot saves me time in small ways throughout the day, enabling me to accomplish more with less effort. And perhaps the most exciting part is knowing that the more we leverage Copilot, the better it will get at automating tasks that free us up to solve bigger problems. I look forward to Copilot familiarizing itself with FOCUS and the use case library to see how far we’re able to go with a natural language description of FinOps questions and tasks.

And of course, this is just the beginning. We’re on the cusp of a revolutionary change to how organizations manage and optimize costs in the cloud. Stay tuned for more updates in the coming months as we share tutorials and samples that will help you streamline and accomplish FinOps tasks in less time. In the meantime, familiarize yourself with Microsoft Fabric and Copilot and learn more about how you can accomplish your FinOps goals with an end-to-end analytics platform.
The post Democratizing FinOps: Transform your practice with FOCUS and Microsoft Fabric appeared first on Azure Blog.
Quelle: Azure

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

In an era where technology is rapidly advancing and information consumption is exponentially growing, there are many new opportunities for businesses to manage, retrieve, and utilize knowledge. The integration of generative AI (content creation by AI) and knowledge retrieval mechanisms is revolutionizing knowledge management, making it more dynamic and readily available. Generative AI offers businesses more efficient ways to capture and retrieve institutional knowledge, improving user productivity by reducing time spent looking for information 

This business transformation was enabled by copilots. Azure AI Studio is the place for AI Developers to build custom copilot experiences.

Copilots infuse data with large language models (LLM) to improve the response generation process. This process can be described as follows: the system receives a query (e.g., a question), then, before responding, fetches pertinent information from a designated data source related to the query, and uses the combined content and query to guide the language model in formulating an appropriate response.

The power of copilots is in their adaptability, particularly their unparalleled ability to seamlessly and securely tap into both internal and external data sources. This dynamic, always-updated integration doesn’t just increase the accessibility and usability of enterprise knowledge, it improves the efficiency and responsiveness of businesses to ever-evolving demands.

Although there is much excitement for copilot pattern-based solutions, it’s important for businesses to carefully consider the design elements to design a durable, adaptable, and effective approach. How can AI developers ensure their solutions do not just capture attention, but also enhance customer engagement? Here are seven pillars to think through when building your custom copilot.

Retrieval: Data ingestion at scale

Data connectors are vital for businesses aiming to harness the depth and breadth of their data across multiple expert systems using a copilot. These connectors serve as the gateways between disparate data silos, connecting valuable information, making accessible and actionable in a unified search experience. Developers can ground models on their enterprise data and seamlessly integrate structured, unstructured, and real-time data using Microsoft Fabric.

For copilot, data connectors are no longer just tools. They are indispensable assets that make real-time, holistic knowledge management a tangible reality for enterprises.

Enrichment: Metadata and role-based authentication

Enrichment is the process of enhancing, refining, and valuing raw data. In the context of LLMs, enrichment often revolves around adding layers of context, refining data for more precise AI interactions, and data integrity. This helps transform raw data into a valuable resource. 

When building custom copilots, enrichment helps data become more discoverable and precise across applications. By enriching the data, generative AI applications can deliver context-aware interactions. 

LLM-driven features often rely on specific, proprietary data. Simplifying data ingestion from multiple sources is critical to create a smooth and effective model. To make enrichment even more dynamic, introducing templating can be beneficial. Templating means crafting a foundational prompt structure, which can be filled in real-time with the necessary data, which can safe-guard and tailor AI interactions.

The combined strength of data enrichment and chunking leads AI quality improvements, especially when handling large datasets. Using enriched data, retrieval mechanisms can grasp cultural, linguistic, and domain-specific nuances. This results in more accurate, diverse, and adaptable responses, bridging the gap between machine understanding and human-like interactions.

Search: Navigating the data maze 

Advanced embedding models are changing the way we understand search. By transforming words or documents into vectors, these models capture the intrinsic meaning and relationships between them. Azure AI Search, enhanced with vector search capabilities, is a leader in this transformation. Using Azure AI Search with the power of semantic reranking gives users contextually pertinent results, regardless of their exact search keywords.

With copilots, search processes can leverage both internal and external resources, absorbing new information without extensive model training. By continuously incorporating the latest available knowledge, responses are not just accurate but also deeply contextual, setting the stage for a competitive edge in search solutions.

The basis of search involves expansive data ingestion, including source document retrieval, data segmentation, embedding generation, vectorization, and index loading to ensure that the results align closely with the user’s intent when a user inputs a query, that undergoes vectorization before heading to Azure AI Search for retrieving most relevant results.

Continuous innovation to refine search capabilities has led to a new concept of hybrid search. This innovative approach melds the familiarity of keyword-based search with the precision of vector search techniques. The blend of keyword, vector, and semantic ranking further improves the search experience, delivering more insightful and accurate results for end users.

Prompts: Crafting efficient and responsible interactions

In the world of AI, prompt engineering provides specific instructions to guide the LLM’s behavior and generate desired outputs. Crafting the right prompt is crucial to get not just accurate, but safe and relevant responses that meet user expectations. 

Prompt efficiency requires clarity and context. To maximize the relevance of AI responses, it is important to be explicit with instructions. For instance, if concise data is needed, specify that you want a short answer. Context also plays a central role. Instead of just asking about market trends, specify current digital marketing trends in e-commerce. It can even be helpful to provide the model with examples that demonstrate the intended behavior.

Azure AI prompt flow enables users to add content safety filters that detect and mitigate harmful content, like jailbreaks or violent language, in inputs and outputs when using open source models. Or, users can opt to use models offered through Azure OpenAI Service, which have content filters built-in. By combining these safety systems with prompt engineering and data retrieval, customers can improve the accuracy, relevance, and safety of their application. 

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Get started with prompt flow chevron_right

Achieving quality AI responses often involves a mix of tools and tactics. Regularly evaluating and updating prompts helps align responses with business trends. Intentionally crafting prompts for critical decisions, generating multiple AI responses to a single prompt, and then selecting the best response for the use case is a prudent strategy. Using a multi-faceted approach helps AI to become a reliable and efficient tool for users, driving informed decisions and strategies.

User Interface (UI): The bridge between AI and users 

An effective UI offers meaningful interactions to guide users through their experience. In the ever-evolving landscape of copilots, providing accurate and relevant results is always the goal. However, there can be instances when the AI system might generate responses that are irrelevant, inaccurate, or ungrounded. A UX team should implement human-computer interaction best practices to mitigate these potential harms, for example by providing output citations, putting guardrails on the structure of inputs and outputs, and by providing ample documentation on an application’s capabilities and limitations. 

To mitigate potential issues like harmful content generation, various tools should be considered. For example, classifiers can be employed to detect and flag possibly harmful content, guiding the system’s subsequent actions, whether that’s changing the topic or reverting to a conventional search. Azure AI Content Safety is a great tool for this.

A core principle for Retrieval Augmented Generation (RAG)-based search experiences is user-centric design, emphasizing an intuitive and responsible user experience. The journey for first-time users should be structured to ensure they comprehend the system’s capabilities, understand its AI-driven nature, and are aware of any limitations. Features like chat suggestions, clear explanations of constraints, feedback mechanisms, and easily accessible references enhance the user experience, fostering trust and minimizing over-reliance on the AI system.

Continuous improvement: The heartbeat of AI evolution 

The true potential of an AI model is realized through continuous evaluation and improvement. It is not enough to deploy a model; it needs ongoing feedback, regular iterations, and consistent monitoring to ensure it meets evolving needs. AI developers need powerful tools to support the complete lifecycle of LLMs, including continuously reviewing and improving AI quality. This not only brings the idea of continuous improvement to life, but also ensures that it is a practical, efficient process for developers. 

Identifying and addressing areas of improvement is a fundamental step to continuously refine AI solutions. It involves analyzing the system’s outputs, such as ensuring the right documents are retrieved, and going through all the details of prompts and model parameters. This level of analysis helps identify potential gaps, and areas for refinement to optimize the solution.

Prompt flow in Azure AI Studio is tailored for LLMs and transforming LLM development lifecycle. Features like visualizing LLM workflows and the ability to test and compare the performance of various prompt versions empowers developers with agility and clarity. As a result, the journey from conceptualizing an AI application to deploying it becomes more coherent and efficient, ensuring robust, enterprise-ready solutions.

Unified development

The future of AI is not just about algorithms and data. It’s about how we retrieve and enrich data, create robust search mechanisms, articulate prompts, infuse responsible AI best practices, interact with, and continuously refine our systems. 

AI developers need to integrate pre-built services and models, prompt orchestration and evaluation, content safety, and responsible AI tools for privacy, security, and compliance. Azure AI Studio offers a comprehensive model catalog, including the latest multimodal models like GPT-4 Turbo with Vision coming soon to Azure OpenAI Service and open models like Falcon, Stable Diffusion, and the Llama 2 managed APIs. Azure AI Studio is a unified platform for AI developers. It ushers in a new era of generative AI development, empowering developers to explore, build, test, and deploy their AI innovations at scale. VS Code, GitHub Codespaces, Semantic Kernel, and LangChain integrations support a code-centric experience.

Whether creating custom copilots, enhancing search, delivering call center solutions, developing bots and bespoke applications, or a combination of these, Azure AI Studio provides the necessary support.

Learn more about the power of Azure AI Studio

As AI continues to evolve, it is essential to keep these seven pillars in mind to help build systems that are efficient, responsible, and always at the cutting-edge of innovation.

Are you eager to tap into the immense capabilities of AI for your enterprise? Start your journey today with Azure AI Studio! 

We’ve pulled together two GitHub repos to help you get building quickly. The Prompt Flow Sample showcases prompt orchestration for LLMOps—using Azure AI Search and Cosmos DB for grounding. Prompt flow streamlines prototyping, experimenting, iterating, and deploying AI applications. The Contoso Website repository houses the eye-catching website featured at Microsoft Ignite, featuring content and image generation capabilities, along with vector search. These two repos can be used together to help build end-to-end custom copilot experiences.

Learn more

Build with Azure AI Studio

Join our SMEs during the upcoming Azure AI Studio AMA session – December 14th, 9-10am PT

Azure AI SDK

Azure AI Studio documentation

Introduction to Azure AI Studio (learn module) 

The post The seven pillars of modern AI development: Leaning into the era of custom copilots appeared first on Azure Blog.
Quelle: Azure

Microsoft Azure delivers purpose-built cloud infrastructure in the era of AI

This year’s Microsoft Ignite brings us together to experience AI transformation in action. AI is driving a new wave of innovation, rapidly changing what applications look like, how they’re designed and built, and how they’re delivered. At the same time, business leaders continue to face challenges, needing to juggle various priorities to offset rising costs, be sustainable, and outmaneuver economic uncertainty. Today’s customers are looking for AI solutions that will meet all their needs.

At Ignite, we’re announcing innovation in Microsoft Azure that is powering more AI capabilities for our customers and helping enterprises with their cloud management and operations. We’re committed to bringing your AI ambitions to production and meeting you where you are. Whether you choose to build hybrid, cloud-native, or open source solutions, we’re rapidly expanding our infrastructure and adding intuitive tools for customers to help take your ideas to production safely and responsibly in this new era of AI. 

With Azure, you can trust that you are on a secure and well-managed foundation to utilize the latest advancements in AI and cloud-native services. Azure is adaptive and purpose-built for all your workloads, helping you seamlessly unify and manage all your infrastructure, data, analytics, and AI solutions. 

Powering groundbreaking AI solutions

The era of AI has largely been shaped by an exponential growth in the sophistication of large language models like OpenAI’s GPT trained on trillions of parameters and groundbreaking generative AI services like Bing Chat Enterprise and Microsoft Copilot used by millions of people globally. The leadership by Azure in optimizing infrastructure for AI workloads in the cloud is pioneering this innovation and why customers like OpenAI, Inflection, and Adept are choosing Azure to build and run AI solutions. 

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Deliver high-powered performance to your most compute-intensive AI workloads chevron_right

In this new era of AI, we are redefining cloud infrastructure, from silicon to systems, to prepare for AI in every business, in every app, for everyone. At Ignite, we’re introducing our first custom AI accelerator series, Azure Maia, designed to run cloud-based training and inferencing for AI workloads such as OpenAI models, Bing, GitHub Copilot, and ChatGPT. Maia 100 is the first generation in the series, with 105 billion transistors, making it one of the largest chips on 5nm process technology. The innovations for Maia 100 span across the silicon, software, network, racks, and cooling capabilities. This equips the Azure AI infrastructure with end-to-end systems optimization tailored to meet the needs of groundbreaking AI such as GPT.

Alongside the Maia 100, we’re introducing our first custom in-house central processing unit series, Azure Cobalt, built on Arm architecture for optimal performance or watt efficiency, powering common cloud workloads for the Microsoft Cloud. From in-house silicon to systems, Microsoft now optimizes and innovates at every layer in the infrastructure stack. Cobalt 100, the first generation in the series, is a 64-bit 128-core chip that delivers up to 40 percent performance improvement over current generations of Azure Arm chips and is powering services such as Microsoft Teams and Azure SQL. 

Networking innovation runs across our first-generation Maia 100 and Cobalt 100 chips. From hollow core fiber technology to the general availability of Azure Boost, we’re enabling faster networking and storage solutions in the cloud. You can now achieve up to 12.5 GBs throughput, 650K input output operations per second (IOPs) in remote storage performance to run data-intensive workloads, and up to 200 GBs in networking bandwidth for network-intensive workloads. 

We continue to build our AI infrastructure in close collaboration with silicon providers and industry leaders, incorporating the latest innovations in software, power, models, and silicon. Azure works closely with NVIDIA to provide NVIDIA H100 Tensor Core (GPU) graphics processing unit-based virtual machines (VMs) for mid to large-scale AI workloads, including Azure Confidential VMs. On top of that, we are adding the latest NVIDIA H200 Tensor Core GPU to our fleet next year to support larger model inferencing with no reduction in latency. 

As we expand our partnership with AMD, customers can access AI-optimized VMs powered by AMD’s new MI300 accelerator early next year. This demonstrates our commitment to adding optionality for customers in price, performance, and power for all of their unique business needs. 

These investments have allowed Azure to pioneer performance for AI supercomputing in the cloud and have consistently ranked us as the number one cloud in the top 500 of the world’s supercomputers. With these additions to the Azure infrastructure hardware portfolio, our platform enables us to deliver the best performance and efficiency across all workloads.

Being adaptive and purpose-built for your workloads

We’ve heard about your challenges in migrating workloads to the public cloud, especially for mission-critical workloads. We continue to work with the technology vendors you’ve relied on to run your workloads and ensure Azure is supporting your needs such as SAP, VMware, NetApp, RedHat, Citrix, and Oracle. We’re excited about our recent partnership to bring Oracle Database Services into Azure to help keep your business efficient and resilient.  

At Ignite, we’re announcing the general availability of Oracle Database@Azure in the US East Azure region as of December 2023. Customers will now have direct access to Oracle database services running on Oracle Cloud Infrastructure (OCI) deployed in Azure data centers. The new service will deliver all the performance, scale, and workload availability advantages of Oracle Exadata Database Service on OCI combined with the security, flexibility, and best-in-class services of Azure. Microsoft is the only other hyper scaler to offer OCI Database Services to simplify cloud migration, multicloud deployment, and management.

As we’ve observed through our interactions the durable state of the cloud is evolving to one where customer workloads need to be supported wherever they’re needed. We realize that cloud migration is not a one-size-fits-all approach, and that’s why we’re committed to meeting you where you are on your cloud journey. An adaptive cloud enables you to thrive in dynamic environments by unifying siloed teams, distributed sites, and sprawling systems into a single operations, application, and data model in Azure.  

Our vision for adaptive cloud builds on the work we’ve already started through Azure Arc. With Azure Arc, customers can project their on-premises, edge, and multicloud resources to Azure, deploy Azure native services on those resources, and extend Azure services to the edge.  

We’re excited to make some new announcements that will help customers implement their adaptive cloud strategies. For VMware customers, we’re announcing the general availability of VMware vSphere enabled by Azure Arc. Azure Arc brings together Azure and the VMware vSphere infrastructure enabling VM administrators to empower their developers to use Azure technologies with their existing server-based workloads and new Kubernetes workloads all from Azure. Additionally, we’re delighted to share the preview of Azure IoT Operations enabled by Azure Arc. By using Azure IoT Operations, customers can greatly reduce the complexity and time it takes to build an end-to-end solution that empowers them to make near real-time decisions backed by AI-driven insights to run agile, resilient, and sustainable operations with both Microsoft and partner technologies.

Amplifying your impact with AI-enhanced operations

Every day, cloud administrators and IT professionals are being asked to do more. We consistently hear from customers that they’re tasked with a wider range of operations, collaborating and managing more users, supporting more complex needs to deliver on increasing customer demand and integrating more workloads into their cloud environment. 

That’s why we’re excited to introduce the public preview of Microsoft Copilot for Azure, a new solution built into Azure that helps simplify how you design, operate, or troubleshoot apps and infrastructure from cloud to edge. Learn how to apply for access to Microsoft Copilot for Azure to see how this new AI companion can help you generate deep insights instantly, discover new cloud functionality, and do complex tasks faster.

Enabling limitless innovation in the era of AI

Delivering on the promise of advanced AI for our customers requires high computing infrastructure, services, and expertise—things that can only be addressed with the scale and agility of the Microsoft Cloud. Our unique equipment and system designs help us and customers like you meet the challenges of the ever-changing technological landscape. From increasing the lifecycle of our hardware and running efficient supply chain operations to providing purpose-built infrastructure in this new era of AI, we can ensure we’re always here to bring your ideas to life in a safe and responsible way.

Learn more about the benefits of Azure infrastructure capabilities at Ignite

Attend these sessions at Ignite to learn more: 

Do more with Windows Server and SQL Server on Azure

Simplifying cloud operations with Microsoft Copilot for Azure

Unlock AI innovation with Azure AI infrastructure 

Check out these resources to help you get started:

Learn more about Azure Migrate and Modernize and Azure Innovate and how they can help you from migration to AI innovation.

Check out the new and free Azure Migrate application and code assessment feature to save on application migrations.

Find out how to take your AI ambitions from ideation to reality with Azure. 

Explore what’s next at Ignite.

The post Microsoft Azure delivers purpose-built cloud infrastructure in the era of AI appeared first on Azure Blog.
Quelle: Azure

Microsoft Azure AI, data, and application innovations help turn your AI ambitions into reality

Welcome to Microsoft Ignite 2023! The past year has been one of true transformation. Companies are seeing real benefits today and are eager to explore what’s next—including how they can do more with their data investments, build intelligent applications, and uncover what AI can do for their business.

We recently commissioned a study through IDC and uncovered insights into how AI is driving business results and economic impact for organizations worldwide. More than 2,000 business leaders surveyed confirmed they’re already using AI for employee experiences, customer engagement, and to bend the curve on innovation.  

The study illustrates the business value of AI but it really comes to life through the stories of how our customers and partners are innovating today. Customers like Heineken, Thread, Moveworks, the National Basketball Association (NBA), and so many more are putting AI technologies to work for their businesses and their own customers and employees. 

From modern data solutions uniquely suited for the era of AI, beloved developer tools, and application services, we’re building Microsoft Azure as the AI supercomputer for customers, no matter the starting point.

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Microsoft Ignite 2023 chevron_right

This week at Ignite, the pace of innovation isn’t slowing down. We’ll share more stories about how organizations are turning to new solutions to drive their business forward. We’re also announcing many new capabilities and updates to make it easier than ever to use your favorite tools, maximize existing investments, save time, and innovate on Azure as a trusted platform.

Modern data solutions to power AI transformation

Every intelligent app starts with data—and your AI is only as good as your data—so a modern data and analytics platform is increasingly important. The integration of data and AI services and solutions can be a unique competitive advantage because every organization’s data is unique.

Last year, we introduced the Microsoft Intelligent Data Platform as an integrated platform to bring together operational databases, analytics, and governance and enable you to integrate all your data assets seamlessly in a way that works for your business.

At Ignite this week, we are announcing the general availability of Microsoft Fabric, our most integrated data and AI solution yet, into the Intelligent Data Platform. Microsoft Fabric can empower you in ways that weren’t possible before with a unified data platform. This means you can bring AI directly to your data, no matter where it lives. This helps foster an AI-centered culture to scale the power of your data value creation so you can spend more time innovating and less time integrating.

EDP is a global energy company that aims to transform the world through renewable energy sources. They’re using Microsoft Fabric and OneLake to simplify data access across data storage, processing, visualization, and AI workflows. This allows them to fully embrace a data-driven culture where they have access to high-value insights and decisions are made with a comprehensive view of the data environment.

We’re also announcing Fabric as an open and extensible platform. We will showcase integrations with many of our partners like LSEG, Esri, Informatica, Teradata and SAS, who have been demonstrating the possibilities of bringing their product experiences as workloads into Fabric, widening their reach and breadth of capabilities.

Every organization is eager to save time and money as they transform. We’re announcing several new features and updates for Azure SQL that make Azure the ideal and most cost-effective place for your data. Updates include lower pricing for Azure SQL Database Hyperscale compute, Azure SQL Managed Instance free trial offer, and a wave of other new features. 

Lufthansa Technik AG has been running Azure SQL to support its application platform and data estate, leveraging fully managed capabilities to empower teams across functions. They’re joining on stage during a breakout session on cloud-scale databases, so you can learn more about their experience directly. 

Easily build, scale, and deploy multimodal generative AI experiences responsibly with Azure

The AI opportunity for businesses is centered on the incredible power of generative AI. We’re inspired by customers who are now nimbly infusing content generation capabilities to transform all kinds of apps into intuitive, contextual experiences that impress and captivate their own customers and employees.

Siemens Digital Industries is one company using Azure AI to enhance its manufacturing processes by enabling seamless communication on the shop floor. Their newest solution helps field engineers report issues in their native language, promoting inclusivity, efficient problem resolution, and faster response times. 

Today organizations need more comprehensive, unified tools to build for this next wave of generative AI-based applications. This is why we’re announcing new updates that push the boundaries of AI innovation and make it easier for customers to responsibly deploy AI at scale across their business.

Everything you need to build, test, and deploy AI innovations in one convenient location

At Ignite, we’re thrilled to introduce the public preview of Azure AI Studio, a groundbreaking platform for AI developers by Microsoft. Everything organizations need to tackle generative AI is now in one place: cutting-edge models, data integration for retrieval augmented generation (RAG), intelligent search capabilities, full-lifecycle model management, and content safety. 

We continue to expand choice and flexibility in generative AI models beyond Azure OpenAI Service. We announced the model catalog at Build and at Ignite, we’re announcing Model as a Service in managed API endpoint coming soon within the model catalog. This will enable pro developers to easily integrate new foundation models like Meta’s Llama 2, G42’s Jais, Command from Cohere and Mistral’s premium models into their applications as an API endpoint and fine-tune models with custom training data, without having to manage the underlying GPU infrastructure. This functionality will help eliminate the complexity for our customers and partners of provisioning resources and managing hosting. 

Large language models (LLM) orchestration and grounding RAG are top of mind as momentum for LLM-based AI applications grows. Prompt flow, an orchestration tool to manage prompt orchestration and LLMOps, is now in preview in Azure AI Studio and generally available in Azure Machine Learning. Prompt flow provides a comprehensive solution that simplifies the process of prototyping, experimenting, iterating, and deploying your AI applications.

We’re also announcing at Ignite that Azure AI Search, formerly Azure Cognitive Search, is now available in Azure AI Studio so everything remains in one convenient location for developers to save time and boost productivity.

Azure AI Content Safety is also available in Azure AI Studio so developers can easily evaluate model responses all in one unified development platform. We’re also announcing the preview of new features inside Azure AI Studio powered by Azure AI Content Safety to address harms and security risks that are introduced by large language models. The new features help identify and prevent attempted unauthorized modifications, and identify when large language models generate material that leverages third-party intellectual property and content. 

With Azure AI Content Safety, developers can monitor human and AI-generated content across languages and modalities and streamline workflows with customizable severity levels and built-in blocklists.

It’s great to see customers already leveraging this to build their AI solutions. In just six months, Perplexity brought Perplexity Ask, a conversational answer engine, to market with Azure AI Studio. They were able to streamline and expedite AI development, get to market faster, scale quickly to support millions of users, and cost-effectively deliver security and reliability.

If you’re creating a custom copilot, improving search, enhancing call centers, developing bots, or a blend of all of this, Azure AI Studio offers everything you need. You can check out Eric Boyd’s blog to learn more about Azure AI Studio.

Generative AI is now multi-modal

We are excited to enable a new chapter in the generative AI journey for our customers with GPT-4 Turbo with Vision, in preview, coming soon to the Azure OpenAI Service and Azure AI Studio. With GPT-4 Turbo with Vision, developers can deliver multi-modal capabilities in their applications. 

We are adding several new updates to Azure AI Vision. GPT-4 Turbo with Vision in combination with our Azure AI Vision service can see, understand, and make inferences like video analysis or video Q&A from visual inputs and associated text-based prompt instructions.

In addition to GPT-4 Turbo with Vision, we are happy to share other new innovations to Azure OpenAI Service including GPT-4 Turbo in preview and GPT-3.5 Turbo 16K 1106 in general availability coming at the end of November and image model DALL-E 3 in preview now.

Search in the era of AI

Effective retrieval techniques, like those powered by search, can improve the quality of responses and response latency. A common practice for knowledge retrieval (retrieval step in RAG), is to use vector search. Search can power effective retrieval techniques to vastly improve the quality of responses and reduce latency, which is essential for generative AI apps as they must be grounded on content from data, or websites, to augment responses generated by LLMs. 

Azure AI Search is a robust information retrieval and search platform that enables organizations to use their own data to deliver hyper-personalized experiences in generative AI applications. We’re announcing the general availability of vector search for fast, highly relevant results from data.

Vector search is a method of searching for information within various data types, including images, audio, text, video, and more. It’s one of the most critical elements of AI-powered, intelligent apps, and the addition of this capability is our latest AI-ready functionality to come to our Azure databases portfolio.

Semantic ranker, formerly known as semantic search, is also generally available and provides access to the same machine learning-powered search re-ranking technology used to power Bing. Your generative AI applications can deliver the highest quality responses to every user Q&A with a feature-rich vector database integrated with state-of-the-art relevance technology.

Accelerate your AI journey responsibly and with confidence

At Microsoft, we’re committed to safe and responsible AI. It goes beyond ethical values and foundational principles, which are critically important. We’re integrating this into the products, services, and tools we release so organizations can build on a foundation of security, risk management, and trust. 

We are pleased to announce new updates at Ignite to help customers pursue AI responsibly and with confidence.

Setting the standard for responsible AI innovation—expanding our Copilot Copyright Commitment

Microsoft has set the standard with services and tools like Azure AI Content Safety, the Responsible AI Dashboard, model monitoring, and our industry-leading commitment to defend and indemnify commercial customers from lawsuits for copyright infringement.   

Today, we are announcing the expansion of the Copilot Copyright Commitment, now called Customer Copyright Commitment (CCC), to customers using Azure OpenAI Service. As more customers build with generative AI inside their organizations, they are inspired by the potential of this technology and are eager to commercialize it externally.   

By extending the CCC to Azure OpenAI Service, Microsoft is broadening our commitment to defend our commercial customers and pay for any adverse judgments if they are sued for copyright infringement for using the outputs generated by Azure OpenAI Service. This benefit will be available starting December 1, 2023. 

 As part of this expansion, we’ve published new documentation to help Azure OpenAI Service customers implement technical measures and other best practices to mitigate the risk of infringing content. Customers will need to comply with the documentation to take advantage of the benefit. Azure OpenAI Service is a developer service and comes with a shared commitment to build responsibly.  We look forward to customers leveraging it as they build their own copilots. 

Announcing the Azure AI Advantage offer

We want to be your trusted partner as you deliver next-gen, transformative experiences with pioneering AI technology, a deeply integrated platform, and leading cloud security.  

Azure offers a full, integrated stack purpose-built for cloud-native, AI-powered applications, accelerating your time to market and giving you a competitive edge and superior performance. ​To help on that journey we are happy to introduce a new offer to help new and existing Azure AI and GitHub Copilot customers realize the value of Azure AI and Azure Cosmos DB together and get on the fast track to developing AI powered applications. You can learn more about the Azure AI Advantage offer and register here. 

Azure Cosmos DB and Azure AI combined deliver many benefits, including enhanced reliability of generative AI applications through the speed of Azure Cosmos DB, a world-class infrastructure and security platform to grow your business while safeguarding your data, and provisioned throughput to scale seamlessly as your application grows.

Azure AI services and GitHub Copilot customers deploying their AI apps to Azure Kubernetes Service may be eligible for additional discounts. Speak to your Microsoft representative to learn more. 

Empowering all developers with AI powered tools

There is so much in store this week at Ignite to improve the developer experience, save time, and increase productivity as they build intelligent applications. Let’s dive into what’s new.

Updates for Azure Cosmos DB—the database for the era of AI

For developers to deliver apps more efficiently and with reduced production costs, at Ignite we’re sharing new features in Azure Cosmos DB.

Now in preview, dynamic scaling provides developers new flexibility to scale databases up or down and brings cost savings to customers, especially those with operations around the globe. We’re also bringing AI deeper into the developer experience and increasing productivity with the preview of Microsoft Copilot for Azure enabling natural language queries in Azure Cosmos DB.  

Bond Brand Loyalty turned to Azure Cosmos DB to scale to more than two petabytes of transaction data while maintaining security and privacy for their own customers. On Azure, Bond built a modern offering to support extensive security configurations, reducing onboarding time for new clients by 20 percent.

We’re announcing two exciting updates to enable developers to build intelligent apps: general availability of both Azure Cosmos DB for MongoDB vCore and vector search in Azure Cosmos DB for MongoDB vCore.

Azure Cosmos DB for MongoDB vCore allows developers to build intelligent applications with full support for MongoDB data stored in Azure Cosmos DB, which unlocks opportunities for app development thanks to deep integration with other Azure services. That means developers can enjoy the benefits of native Azure integrations, low total cost of ownership (TCO), and a familiar vCore architecture when migrating existing applications or building new ones. 

Vector search in Azure Cosmos DB for MongoDB vCore allows developers to seamlessly integrate data stored in Azure Cosmos DB into AI-powered applications, including those using Azure OpenAI Service embeddings. Built-in vector search enables you to efficiently store, index, and query high-dimensional vector data, and eliminates the need to transfer the data outside of your Azure Cosmos DB database.

PostgreSQL developers have used built-in vector search in Azure Database for PostgreSQL and Azure Cosmos DB for PostgreSQL since this summer. Now, they can take advantage of the public preview of Azure AI extension in Azure Database for PostgreSQL to build LLMs and rich generative AI solutions.

KPMG Australia used the vector search capability when they turned to Azure OpenAI Service and Azure Cosmos DB to build their own copilot application. The KymChat app has helped employees speed up productivity and streamline operations. The solution is also being made available to KPMG customers through an accelerator that combines KymChat’s use cases, features, and lessons learned, helping customers accelerate their AI journey.

Building cloud-native and intelligent applications

Intelligent applications combine the power of AI and cloud-scale data with cloud-native app development to create highly differentiated digital experiences. The synergy between cloud-native technologies and AI is a tangible opportunity for evolving traditional applications, making them intelligent, and delivering more value to end users. We’re dedicated to continually enhancing Azure Kubernetes Service to meet these evolving demands of AI for customers who are just getting started as well as those who are more advanced.

Customers can now run specialized machine learning workloads like LLMs on Azure Kubernetes Service more cost-effectively and with less manual configuration. The Kubernetes AI toolchain Operator automates LLMs deployment on AKS across available CPU and GPU resources by selecting optimally sized infrastructure for the model. It makes it possible to easily split inferencing across multiple lower-GPU-count virtural machines (VMs) thus increasing the number of Azure regions where workloads can run, eliminating wait times for higher GPU-count VMs, and lowering overall cost. Customers can also run preset models from the open source hosted on AKS, significantly reducing costs and overall inference service setup time while eliminating the need for teams to be experts on available infrastructure. 

Azure Kubernetes Fleet Manager is now generally available and enables multi-cluster and at-scale scenarios for Azure Kubernetes Service clusters. Fleet manager provides a global scale for admins to manage workload distribution across clusters and facilitate platform and application updates so developers can rest assured they are running on the latest and most secure software. 

We’ve also been sharing learnings about how to help engineering organizations enable their own developers to get started and be productive quickly, while still ensuring systems are secure, compliant, and cost-controlled. Microsoft is providing a core set of technology building blocks and learning modules to help organizations get started on their journey to establish a platform engineering practice. 

New Microsoft Dev Box capabilities to improve the developer experience

Maintaining a developer workstation that can build, run, and debug your application is critical to keeping up with the pace of modern development teams. Microsoft Dev Box provides developers with secure, ready-to-code developer workstations for hybrid teams of any size. 

We’re introducing new preview capabilities to give development teams more granular control over their images, the ability to connect to Hosted Networks to simplify connecting to your resources securely, and templates to make it easier to get up and running. Paired with new capabilities coming to Azure Deployment Environments, it’s easier than ever to deploy those projects to Azure.

Build upon a reliable and scalable foundation with .NET 8

.NET 8 is a big leap forward towards making .NET one of the best platforms to build intelligent cloud-native applications, with the first preview of .NET Aspire – an opinionated cloud ready stack for building observable, production ready, distributed cloud native applications. It includes curated components for cloud-native fundamentals including telemetry, resilience, configuration, and health checks. The stack makes it easier to discover, acquire, and configure essential dependencies for cloud-native applications on day 1 and day 100. 

.NET 8 is also the fastest version of .NET ever, with developer productivity enhancements across the stack – whether you are building for cloud, a full stack web app, a desktop or mobile app suing .NET MAUI, or integrating AI to build the next copilot for your app. These are available in Visual Studio, which also releases today.  

Azure Functions and Azure App Service have full support for .NET 8 both in Linux and Windows, and both Azure Kubernetes Service and Azure Container Apps also support .NET 8 today.  

There are no limits to your innovation potential with Azure

There’s so much rolling out this week with data, AI, and digital applications so I hope you’ll tune into the virtual Ignite experience and hear about the full slate of announcements and more about how you can put Azure to work for your business. 

This week’s announcements are proof of our commitment to helping customers take that next step of innovation and stay future-ready. I can’t wait to see how your creativity and new innovations unfold for your business. 

You can check out these resources to learn more about everything shared today. We hope you have a great Ignite week!

Attend these sessions to learn more about how Azure can help you, no matter the starting point: 

Keynote session with Scott Guthrie and friends: AI transformation for your organization with the Microsoft Cloud 

Make your data AI ready with Microsoft Fabric and Azure Databricks 

Build ISV apps with Microsoft Fabric in the Intelligent Data Platform 

AI and Kubernetes: A winning combination for Modern App Development 

Build your own Copilot with Azure AI Studio

What’s new in generative AI? 

Vector search and state of the art retrieval for Generative AI apps

Master Platform Engineering: Architecting Scalable and Resilient Systems 

Explore all the Microsoft Azure announcements in the Book of News.

Learn how Microsoft Azure delivers purpose-built cloud infrastructure in the era of AI

Explore Microsoft’s Responsible AI playbook to learn more about our approach, what we learned, and how it can apply to your business. 

Learn more about Azure Migrate and Modernize and Azure Innovate and how they can help you from migration to AI innovation. 

Get ready for what’s next by visiting the AI Learning and Community Hub on Microsoft Learn with AI skilling opportunities on Microsoft Learn.

 

 
The post Microsoft Azure AI, data, and application innovations help turn your AI ambitions into reality appeared first on Azure Blog.
Quelle: Azure

Advancing hybrid cloud to adaptive cloud with Azure

The pace of change in the world around us is incredible. Between the impact of new, transformational technology, fluctuations in the economic landscape, and the hybrid nature of the post-COVID-19 world—we see customers across every industry taking action to innovate and adapt at this important inflection point. Entering the era of AI, the pace of change will only accelerate.

Our customers constantly innovate to keep pace with rapid market shifts and technological advancements, but they require a common innovation and operations platform that spans business initiatives. They’re finding that in the rush to innovate, they’ve spun up different projects and initiatives throughout the organization—each with its own approach. Specifically, they are asking us for help in three large areas: 

Sprawling systems: Most companies are dealing with an explosion of resources. Servers and devices in the operational edge and IoT, in addition to multicloud deployments, can be overwhelming. Basic tasks like patching, configuring, and securing get exponentially harder with every new location and technology.

Siloed teams: Rapid innovation is happening in every business unit—usually in an uncoordinated way. Often, there’s little chance to share work or learnings. Compounding matters, IT, development, and operational technology (OT) teams also tend to run separate technology initiatives and roll out new technology in an uncoordinated manner resulting in duplicated effort and increased financial and security risk. Over time, the silos unintentionally entrench talents in a single project or tech stack, artificially limiting their impact.

Technical debt: Short-term solutions, without a comprehensive long-term strategy, often result in systems incompatibility that keeps valuable data trapped where it’s created and can’t be leveraged to improve the business.

The need for a unified platform and system to address these challenges is evident. We believe Azure is the platform that can help, and we have been investing in Azure Arc to solve these problems. We see an opportunity to do more by bringing together agility and intelligence so that our customers can proactively adapt to change, rather than react to it and maintain a competitive edge in a dynamic landscape. 

The adaptive cloud approach for Azure

We are excited about the momentum we have with Azure Arc. There are over 21,000 active customers, and we’re continuing to see excellent growth. With Azure Arc, we crossed the boundaries of existing market categories, whether that is hybrid, multicloud, edge, or IoT. Customers aspire to the next level of modularity, integration, and simplicity. We believe that our customers can achieve this aspiration with a new approach which we call adaptive cloud.

The adaptive cloud approach unifies siloed teams, distributed sites, and sprawling systems into a single operations, security, application, and data model, enabling organizations to leverage cloud-native and AI technologies to work simultaneously across hybrid, multicloud, edge, and IoT.

An adaptive cloud approach shifts organizations from a reactive posture to one of proactive evolution, enabling people to anticipate and act upon changes in market trends, customer needs, and technological advancements ahead of time. This strategic foresight enables businesses to pivot quickly, embrace continuous improvement, and integrate new technologies seamlessly. By building resilience into their operational models, businesses can optimize resource usage and mitigate security and business risks before they manifest.

Innovative Azure capabilities

Azure is adopting the adaptive cloud approach by building on the work we have started with Azure Arc, as an extension of Azure Resource Manager (ARM). Azure Resource Manager keeps track of a rich set of configurations, logs, and metrics for every resource in Azure. It is a single source of truth for your Azure investments. Azure Arc enables you to project hybrid, multicloud, edge, and IoT resources to Azure Resource Manager. Not only can you create a single source of truth, but you can easily apply cloud services across your globally distributed digital estate. For example, observability tools in Azure, like Azure Monitor, give you visibility across thousands of assets in one place. Our security offerings and features, like Microsoft Defender for Cloud, Microsoft Sentinel, or Azure Policy for security enforcement, enable you to develop and improve your security posture. You can accomplish a lot with Azure Arc today but you will be able to do even more as we are envisioning a world where you can leverage AI to amplify your impact across existing and new scenarios.

Figure 1: A mix of Azure and Azure Arc enabled servers in Microsoft Defender for Cloud

Operate with AI-enhanced central management and security

Over the last few years, we’ve been investing in making many core Azure management and security capabilities available through Azure Arc. Having central visibility and security is the most common scenario our customers are taking advantage of. Features and services like role-based access control, Azure Policy, Key Vault, Microsoft Defender for Cloud, Microsoft Sentinel, and Azure Monitor are all available today to use across your digital estate.

We just announced the preview of Microsoft Copilot for Azure. Going forward, Copilot will be able to reason over the information you put directly into Azure Resource Manager or other services, like Azure Monitor, regardless of the location of those resources.

From there, you will be able to transform the way you work across the digital estate. Troubleshooting, for example, can be tedious. Going through logs, sending emails, and reading documentation can be monotonous. Copilot enables you to analyze the resource metrics and explore resource configuration and status at scale. Copilot also enables deeper exploration and intelligent assessment, such as anomaly detection of unexpected changes, and provides recommendations to address the issues from cloud to edge.

Figure 2: Using Copilot to get status across heterogeneous environments during active troubleshooting.

For example, with Azure Stack HCI version 23H2 preview, you can use Microsoft Copilot for Azure (preview) to identify problems and get information about your Azure Stack HCI clusters. When you ask Copilot for information about your edge infrastructure, it automatically pulls context when possible, based on the current conversation or based on the page you’re viewing in the Azure portal.

At VMware Explore EU, we announced the general availability of VMware vSphere capabilities enabled by Azure Arc. Customers can simplify the management of their VMware vSphere resources with the Azure Resource Manager functionality.

AI-enhanced management significantly elevates IT, enabling teams to discover new capabilities and new scenarios, so they can focus on strategic tasks and less on administrative chores. Copilot is your universal AI assistant that facilitates a streamlined and consistent set of functionalities for collaboration and is integrated seamlessly with the Azure portal and a variety of management tools.

The World Bank is one of the companies whose vast and distributed operations really called for a central and standardized approach. They are among the world’s largest sources of funding and financial knowledge for 189 developing countries. The World Bank employs a diverse workforce representing more than 170 countries in more than 130 locations. Recognizing an opportunity to improve efficiency and reduce costs, they were looking for a cloud-based solution that would offer centralized monitoring, performance, resource consumption, and security management—all in a single package. They chose Azure Arc to build their solution—in particular, because of their investment in SQL Server.

“We wanted to implement Azure Arc so we could utilize all the features and manage all our on-premises and cloud servers, including AWS, from one location,” Kala Macha explains. “With Azure Arc, we can manage everything at the operating system level and on the SQL Server side as well—all from a single pane of glass. It’s made a huge difference in our efficiency.”

Rapidly develop and scale applications across boundaries

We aim to break free from outdated system limitations and make it effortless for you to adopt advanced, flexible technologies. The ability to easily use your choice of cloud-native tools, containers, and other services will help accelerate digital transformation throughout your organization. With a standardized application model, with Kubernetes and Azure services, you can scale applications from the massive cloud platforms to on-site production without complex rewrites. A unified application deployment strategy, complemented by streamlined DevOps integration processes promotes collaboration and efficiency.

Figure 3: Connect, manage, and operate Kubernetes from Azure

New applications are increasingly being packaged and distributed as container images, making Kubernetes clusters among the most popular Azure Arc-enabled resources. Today, you can use Azure Arc to connect Cloud Native Computing Foundation (CNCF) Kubernetes to Azure to be operated centrally and at scale. Azure Kubernetes Service (AKS) has been enabled by Azure Arc to provide options to run a managed Kubernetes solution at the edge, with built-in support for Linux and Windows containers.

At Ignite we announced more deployment options for AKS to run with significantly reduced overhead when creating clusters. Every AKS cluster provisioned through Azure Arc is automatically configured with the Kubernetes agents inside, enabling access to extensions like Microsoft Defender, Azure Monitor, GitOps, and others. You can easily provision and manage Kubernetes clusters in the Azure portal, directly from the Azure Kubernetes Services resource view, Azure CLI, or (ARM)/Bicep templates for automation. You can also provision AKS clusters from the Azure Stack HCI resource view, and in the future from third-party infrastructure that has been enabled using Azure Arc.

Recently we spoke with one of our cutting-edge customers who’s driving innovation at scale, DICK’S Sporting Goods, a leading retailer with over 800 locations. DICK’S Sporting Goods is enhancing in-store experiences through apps—such as one that analyzes golf and baseball bat swings and recommends products best suited to each individual athlete. By integrating their stores with Azure, they can swiftly roll out new features built in the cloud to customers everywhere. Watch here to learn more.

“Our unified store strategy is all about rapid innovation. It lets us respond to market shifts and customer feedback in minutes, ensuring our stores are always current,” says Jon Hathaway, Sr Director – Platform and Infrastructure.

Cultivate data and insights across physical operations

Physical operations environments, like factories and retail storefronts, are the backbone of many businesses. Given the dispersed nature of these sites, customers are eager to deploy AI on a global basis to enable higher levels of productivity, efficiency, and sustainability. A unified data foundation cultivates data and insights across physical operations, driving more efficient workflows, predictive insights, and resource optimization.

For customers across industries, the ability to connect the physical world to the digital world is a foundational step in the digital transformation journey. To help them scale the transformation of their physical operations, we are announcing a new offering in public preview called Azure IoT Operations. Enabled by Azure Arc, Azure IoT Operations expands on our IoT portfolio with a composable set of services that help organizations onboard IT and OT assets, capture, evolve, and unify insights, and take actions to drive transformation at scale.

Azure IoT Operations empowers our customers with a unified, enterprise-wide technology architecture and data plane that supports repeatable solution deployment and comprehensive AI-enhanced decision-making. It enables a cloud-to-edge data plane with local data processing and analytics to transfer clean, useful data to hyperscale cloud services such as Microsoft Fabric, Azure Event Grid, and Azure Digital Twins. A common data foundation is essential to democratize data, enable cross-team collaboration, and accelerate decision-making.

One of the customers we are working with on their physical operations data strategy is Grupo Bimbo. Bimbo Bakeries USA (BBU), part of Grupo Bimbo a multinational food company with 217 plants in 34 countries globally. BBU takes pride in its product, zealously safeguarding high standards and quality. To turn out millions of loaves every day, they depend on metrics that illustrate everything from machine speeds and temperatures to downtime. Bimbo is always looking for innovative ways to produce the quality products their customers expect from them. 

BBU is leveraging Azure IoT Operations to improve its current Industrial IoT (IIOT) solution and tackle the challenge of IT/OT convergence. Azure IoT Operations provides seamless data flow from process and equipment; everything from machine speeds and oven temperatures to equipment downtime. This new platform will enable robust data processing so that BBU can get visibility into near real-time production data that allows them to make timely adjustments, therefore maximizing production efficiencies.

The future is now

Customers can start applying the adaptive cloud approach to drive seamless transformation from cloud to edge today. Experience the latest offerings below and see them in action by visiting Azure Arc Jumpstart where you can learn and try many different scenarios.
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Quelle: Azure