Kategorie: Microsoft Azure

Microsoft named a Leader in 2023 Gartner® Magic Quadrant™ for Strategic Cloud Platform Services (SCPS)

We are honored to be recognized by Gartner® as a Leader in the recently published 2023 Gartner® Magic Quadrant™ for Strategic Cloud Platform Services (SCPS). In the report, Gartner placed Microsoft furthest in Completeness of Vision.

For years, we’ve understood that the industry trusts Gartner Magic Quadrant reports to provide a holistic review of cloud providers’ capabilities. We’re pleased by this placement in the Gartner report as we continue to prioritize investments to make Azure the global cloud computing platform powering transformation and growth—enabling new possibilities for organizations to embrace the latest technologies and to advance at a rapid pace. With highly secure, state-of-the-art Azure datacenters designed with data residency in mind, Azure hosts one of the most advanced supercomputers in the world.

The Gartner report validates our commitment to empowering customers to reach new heights. We are proud of our purpose-built cloud infrastructure for the era of AI, one that is adaptive across on-prem, multicloud and edge environments, for our complete development platform with advanced tools to accelerate developer productivity, for our AI services and tools to empower innovation, and for our extensive partnerships across a wide range of industry leaders to give customers the choices they desire.

We are honored for this recognition and will continue to build the future together with our customers, no matter where they are in the cloud journey.

Purpose-built cloud infrastructure for the era of AI

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. We’ve also expanded our partnership with AMD, enabling our customers with choices to meet 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.

An adaptive cloud across on-prem, multicloud and edge environments

The cloud is evolving to support customer workloads wherever they’re needed. We realize cloud migration is not a one-size-fits-all approach, and that’s why we’re committed to meeting customers where they are in their cloud journey. With Azure you have an adaptive cloud that enables you to thrive in dynamic environments by unifying siloed teams, distributed sites, and sprawling systems into single operations, application, and data model in Azure. 

Azure Arc helps customers implement their adaptive cloud strategies, providing a bridge that extends the Azure platform and enables them to build applications and services across datacenters, at the edge, and in multicloud environments. Through a portfolio of services, tools, and infrastructure, organizations can take advantage of Azure services within a single control plane. And with the recent general availability of VMware vSphere enabled by Azure Arc that brings together Azure and the VMware vSphere infrastructure, VMware administrators can empower their developers to use Azure technologies with their existing server-based workloads and new Kubernetes workloads all from Azure.

Every day, cloud administrators and IT professionals are being asked to do more. We consistently hear from customers they’re tasked with a wider range of operations; they are required to collaborate with more users, and support more complex needs to deliver on increasing customer demand—all while integrating more workloads into their cloud environment. To support our customers, we recently launched the public preview of Microsoft Copilot for Azure, a new solution built into Azure that will help simplify how they design, operate, or troubleshoot apps and infrastructure from cloud to edge.

One location to build, test, and deploy AI innovations securely

We’re only just starting to understand the potential of generative AI and how it will transform the way we live and work. Developers are at the heart of this new wave of innovation, pushing the boundaries of what’s possible. With a cloud-first approach developers spend less time on maintaining apps and infrastructure and more time on innovating and ideating, reducing the time to market. What’s more, developers can build with confidence, knowing that Azure has built-in tools and technologies to help ensure a secure and responsible approach from development to deployment.

The public preview of Azure AI Studio gives developers everything they need to build, test, and deploy AI innovations in one convenient location: cutting-edge models, data integration for retrieval augmented generation (RAG), intelligent search capabilities, full-lifecycle model management, and content safety. Azure AI Content Safety is available in Azure AI Studio so developers can easily evaluate model responses all in one unified development platform to quickly and efficiently detect offensive or inappropriate content in text and images, Customers like Heineken, Thread, Moveworks, Manulife , and so many more are putting Azure AI technologies to work for their businesses and their own customers and employees.

Integrated, AI-based tools to help developers innovate efficiently

The integration of AI-based tools in the development cycle is not just accelerating innovation, but also enabling developers to spend more time on strategic, meaningful work, and less time on tasks like debugging and infrastructure management. With Microsoft DevBox, developers can streamline development with secure, ready-to-code workstations in the cloud, leveraging self-service access to high-performance, cloud-based workstations preconfigured and ready-to-code for specific projects. GitHub Copilot uses AI technology to suggest code in the editor maximizing time spent on business logic over boilerplate, with developers reporting they can complete tasks up to 55% faster and feel up to 88% more productive.

With tools that are designed to work seamlessly together, Microsoft’s complete development platform stack empowers developers with flexible solutions, so they can build next-gen apps productively and securely, where they want. GitHub integrates with Azure to provide a continuous integration and deployment (CI/CD) pipeline for developers, and with GitHub Enterprise they can be more efficient with up to 75% improvement in time spent managing tools and code infrastructure. Customers like GM are collaborating with Microsoft to help speed up innovation within their organization, test and learn, and create agile environments using Microsoft development platforms such as GitHub, Visual Studio and Microsoft DevBox. 

Create differentiated AI experiences with cloud-native apps

Azure’s cloud-native platform is the best place to run and scale applications while seamlessly embedding Azure’s native AI services. Azure gives developers the choice between control and flexibility, with complete focus on productivity regardless of what option is chosen. Azure App Service allows developers to host .NET, Java, Node.js, and Python web apps and APIs in a fully managed Azure service. Azure takes care of all the infrastructure management like high availability, load balancing, and autoscaling enabling developers to accelerate app development to production up to 50 percent with fully-managed Azure App Service. Developers can further streamline the development process for faster time to market with cloud-based tools and services including Azure Kubernetes Service (AKS), GitHub Enterprise and Advanced Security, Azure Cosmos DB, and Azure Cognitive Services. And with Microsoft Copilot for Azure, developers have an AI companion to help them design, operate, optimize, and troubleshoot everyday tasks with AKS and Kubernetes. Customers such as Sapiens have leveraged the synergy between cloud-native technologies and AI to accelerate their digital transformation and deliver more value to their end users with intelligent apps.

Building the future together

We are dedicated to empowering our customers with technology that unlocks limitless innovation, helping them wherever they are on their technology journey. We use the decades of experience we have in migrating Microsoft’s on-premises workloads to the cloud, to inform how we make it easier for customers and partners to use the cloud—from how we build products, to the real-world migration guidance we provide. It’s why 95 percent of Fortune 500 companies trust Azure with their business on Azure. Customers like AT&T rely on Azure AI for Enterprise Chat GPT and better knowledge mining and the World Bank who is using Azure cloud-based solution to centralized monitoring, performance, resource consumption, and security management across clouds, all in a single package.

And we are not doing this alone. We have a vast global partner network and a growing number of technology partnerships across a wide range of industry leaders such as, Databricks, Netapp, NVIDIA, Oracle, OpenAI, SAP, Snowflake, VMware, and others. We recently announced a partnership to bring Oracle Database Services into Azure to help maximize efficiency and resiliency for our mutual customers’ businesses. Our investments with SAP continue to grow, enhancing performance and resilience for mission critical workloads with new powerful infrastructure options for our SAP customers such as the Azure M-series Mv3 family, the next generation of memory optimized virtual machines (VMs). As we expand partnerships with OpenAI, Meta and Hugging Face, we create more opportunities for organizations and developers to build generative AI experiences, offering the most comprehensive selection of frontier, open and commercial models.

As Microsoft continues to innovate at the speed of AI, Azure is at the foundation of all our innovation, powering all aspects of the Microsoft Cloud and our copilots. Azure makes it possible for organizations to securely embrace the latest technologies and leverage them to create new ones. We endlessly optimize our infrastructure to bring faster, secure, reliable, and more sustainable computing power, so that our customers and partners can build with confidence. Linked by one of the largest interconnected networks on the planet, we’re providing unprecedented scalability, low latency, data residency, and high availability to our customers around the world. Azure provides the cloud platform wherever you are with highly secure, state-of the art Azure datacenters, offering 60+ regions—more than any other cloud provider.

With Azure, customers can trust they are on a secure and well-managed foundation to utilize the latest advancements in AI and cloud-native services, safely and responsibly, to create today’s solutions and tomorrow’s breakthroughs. We are dedicated to the success of our customers and partners, and continue to invest in ways that ensure Azure is the leading choice for customers, big and small around the globe.

Discover resources for your cloud journey

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. 

For the latest Azure innovations watch our Ignite 2023 sessions on demand.

Disclaimer: 

Gartner, Magic Quadrant for Strategic Cloud Platform Services, David Wright, Dennis Smith, and 4 more, 4 December 2023.

The report was previously known as Magic Quadrant for Cloud Infrastructure and Platform Services (2020-2022) and Magic Quadrant for Cloud Infrastructure as a Service till 2019. 

Gartner is a registered trademark and service mark 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 here.

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 2023 Gartner® Magic Quadrant™ for Strategic Cloud Platform Services (SCPS) appeared first on Azure Blog.
Quelle: Azure

Infuse responsible AI tools and practices in your LLMOps

This is the third blog in our series on LLMOps for business leaders. Read the first and second articles to learn more about LLMOps on Azure AI.

As we embrace advancements in generative AI, it’s crucial to acknowledge the challenges and potential harms associated with these technologies. Common concerns include data security and privacy, low quality or ungrounded outputs, misuse of and overreliance on AI, generation of harmful content, and AI systems that are susceptible to adversarial attacks, such as jailbreaks. These risks are critical to identify, measure, mitigate, and monitor when building a generative AI application.

Note that some of the challenges around building generative AI applications are not unique to AI applications; they are essentially traditional software challenges that might apply to any number of applications. Common best practices to address these concerns include role-based access (RBAC), network isolation and monitoring, data encryption, and application monitoring and logging for security. Microsoft provides numerous tools and controls to help IT and development teams address these challenges, which you can think of as being deterministic in nature. In this blog, I’ll focus on the challenges unique to building generative AI applications—challenges that address the probabilistic nature of AI.

First, let’s acknowledge that putting responsible AI principles like transparency and safety into practice in a production application is a major effort. Few companies have the research, policy, and engineering resources to operationalize responsible AI without pre-built tools and controls. That’s why Microsoft takes the best in cutting edge ideas from research, combines that with thinking about policy and customer feedback, and then builds and integrates practical responsible AI tools and methodologies directly into our AI portfolio. In this post, we’ll focus on capabilities in Azure AI Studio, including the model catalog, prompt flow, and Azure AI Content Safety. We’re dedicated to documenting and sharing our learnings and best practices with the developer community so they can make responsible AI implementation practical for their organizations.

Mapping mitigations and evaluations to the LLMOps lifecycle

We find that mitigating potential harms presented by generative AI models requires an iterative, layered approach that includes experimentation and measurement. In most production applications, that includes four layers of technical mitigations: (1) the model, (2) safety system, (3) metaprompt and grounding, and (4) user experience layers. The model and safety system layers are typically platform layers, where built-in mitigations would be common across many applications. The next two layers depend on the application’s purpose and design, meaning the implementation of mitigations can vary a lot from one application to the next. Below, we’ll see how these mitigation layers map to the large language model operations (LLMOps) lifecycle we explored in a previous article.

Fig 1. Enterprise LLMOps development lifecycle.

Ideating and exploring loop: Add model layer and safety system mitigations

The first iterative loop in LLMOps typically involves a single developer exploring and evaluating models in a model catalog to see if it’s a good fit for their use case. From a responsible AI perspective, it’s crucial to understand each model’s capabilities and limitations when it comes to potential harms. To investigate this, developers can read model cards provided by the model developer and work data and prompts to stress-test the model.

Model

The Azure AI model catalog offers a wide selection of models from providers like OpenAI, Meta, Hugging Face, Cohere, NVIDIA, and Azure OpenAI Service, all categorized by collection and task. Model cards provide detailed descriptions and offer the option for sample inferences or testing with custom data. Some model providers build safety mitigations directly into their model through fine-tuning. You can learn about these mitigations in the model cards, which provide detailed descriptions and offer the option for sample inferences or testing with custom data. At Microsoft Ignite 2023, we also announced the model benchmark feature in Azure AI Studio, which provides helpful metrics to evaluate and compare the performance of various models in the catalog.

Safety system

For most applications, it’s not enough to rely on the safety fine-tuning built into the model itself. large language models can make mistakes and are susceptible to attacks like jailbreaks. In many applications at Microsoft, we use another AI-based safety system, Azure AI Content Safety, to provide an independent layer of protection to block the output of harmful content. Customers like South Australia’s Department of Education and Shell are demonstrating how Azure AI Content Safety helps protect users from the classroom to the chatroom.

This safety runs both the prompt and completion for your model through classification models aimed at detecting and preventing the output of harmful content across a range of categories (hate, sexual, violence, and self-harm) and configurable severity levels (safe, low, medium, and high). At Ignite, we also announced the public preview of jailbreak risk detection and protected material detection in Azure AI Content Safety. When you deploy your model through the Azure AI Studio model catalog or deploy your large language model applications to an endpoint, you can use Azure AI Content Safety.

Building and augmenting loop: Add metaprompt and grounding mitigations

Once a developer identifies and evaluates the core capabilities of their preferred large language model, they advance to the next loop, which focuses on guiding and enhancing the large language model to better meet their specific needs. This is where organizations can differentiate their applications.

Metaprompt and grounding

Proper grounding and metaprompt design are crucial for every generative AI application. Retrieval augmented generation (RAG), or the process of grounding your model on relevant context, can significantly improve overall accuracy and relevance of model outputs. With Azure AI Studio, you can quickly and securely ground models on your structured, unstructured, and real-time data, including data within Microsoft Fabric.

Once you have the right data flowing into your application, the next step is building a metaprompt. A metaprompt, or system message, is a set of natural language instructions used to guide an AI system’s behavior (do this, not that). Ideally, a metaprompt will enable a model to use the grounding data effectively and enforce rules that mitigate harmful content generation or user manipulations like jailbreaks or prompt injections. We continually update our prompt engineering guidance and metaprompt templates with the latest best practices from the industry and Microsoft research to help you get started. Customers like Siemens, Gunnebo, and PwC are building custom experiences using generative AI and their own data on Azure.

Fig 2. Summary of responsible AI best practices for a metaprompt.

Evaluate your mitigations

It’s not enough to adopt the best practice mitigations. To know that they are working effectively for your application, you will need to test them before deploying an application in production. Prompt flow offers a comprehensive evaluation experience, where developers can use pre-built or custom evaluation flows to assess their applications using performance metrics like accuracy as well as safety metrics like groundedness. A developer can even build and compare different variations of their metaprompts to assess which may result in the higher quality outputs aligned to their business goals and responsible AI principles.

Fig 3. Summary of evaluation results for a prompt flow built in Azure AI Studio.

Fig 4. Details for evaluation results for a prompt flow built in Azure AI Studio.

Operationalizing loop: Add monitoring and UX design mitigations

The third loop captures the transition from development to production. This loop primarily involves deployment, monitoring, and integrating with continuous integration and continuous deployment (CI/CD) processes. It also requires collaboration with the user experience (UX) design team to help ensure human-AI interactions are safe and responsible.

User experience

In this layer, the focus shifts to how end users interact with large language model applications. You’ll want to create an interface that helps users understand and effectively use AI technology while avoiding common pitfalls. We document and share best practices in the HAX Toolkit and Azure AI documentation, including examples of how to reinforce user responsibility, highlight the limitations of AI to mitigate overreliance, and to ensure users are aware that they are interacting with AI as appropriate.

Monitor your application

Continuous model monitoring is a pivotal step of LLMOps to prevent AI systems from becoming outdated due to changes in societal behaviors and data over time. Azure AI offers robust tools to monitor the safety and quality of your application in production. You can quickly set up monitoring for pre-built metrics like groundedness, relevance, coherence, fluency, and similarity, or build your own metrics.

Looking ahead with Azure AI

Microsoft’s infusion of responsible AI tools and practices into LLMOps is a testament to our belief that technological innovation and governance are not just compatible, but mutually reinforcing. Azure AI integrates years of AI policy, research, and engineering expertise from Microsoft so your teams can build safe, secure, and reliable AI solutions from the start, and leverage enterprise controls for data privacy, compliance, and security on infrastructure that is built for AI at scale. We look forward to innovating on behalf of our customers, to help every organization realize the short- and long-term benefits of building applications built on trust.

Learn more

Explore Azure AI Studio.

Watch the 45-minute breakout session on “Evaluating and designing Responsible AI Systems for the Real World” and “End-to-End AI App Development: Prompt Engineering to LLMOps” from Microsoft Ignite 2023.

Take the 45-minute Introduction to Azure AI Studio course on Microsoft Learn.

The post Infuse responsible AI tools and practices in your LLMOps appeared first on Azure Blog.
Quelle: Azure

Create new ways to serve your mission with Microsoft Azure Space

Since launching Microsoft Azure Space, we’ve been focused on three main goals:

Connect anyone, anywhere, at any security level, back to the full power and potential of the Microsoft Cloud. This includes working with exciting space start-ups like Muon Space and True Anomaly as well as government agencies like the United States Space Force.

Enable real-time analysis across petabytes of data gathered on orbit, so that our customers can take immediate action that delivers on their mission.

Empower developers to develop, deploy, and run their applications on orbit.

As customers and partners have adopted and experimented with the Azure Space portfolio, new and interesting use cases are emerging that illustrate what’s possible. Today, we are excited to share some of those customer stories, along with updates for Azure Orbital Ground Station, Azure Orbital’s software development kit, and Microsoft Planetary Computer. While it is still early days, these stories offer a glimpse at understanding how an accessible space layer can transform the way organizations across the public and private sectors serve their missions.

Satellite operators are using Azure Orbital Ground Station for spacecraft communications

Delivering space data to Earth requires a secure, robust ground network with low latency and high throughput—presenting various challenges for the operator. Opportunities for satellite contacts are limited by ground station coverage, and it can be difficult and expensive to achieve sufficient capacity.

Azure Space is enabling partner-powered, space-to-cloud transmissions with end-to-end support for space data downlink, processing, storage, analytics, and dissemination. Azure Orbital Ground Station provides easy, secure access to communication products and services required to support all phases of satellite missions—from launch to operations and decommissioning. Mission operations are seamless with self-service scheduling of contacts in Microsoft Azure with a managed data path.

Muon Space collaborates with Microsoft for its first two launches

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Accelerating the pace of innovation with Azure Space and our partners chevron_right

As previously announced, Muon Space selected Microsoft to support its first-ever launch, using Azure Orbital Ground Station as the sole ground provider for their MuSat-1 mission. Muon Space is ramping up for the launch of its second satellite, MuSat-2, in early 2024—again leveraging Azure Orbital Ground Station to bring down data gathered by a prototype microwave sensor. Muon Space will provide space weather and ionospheric data to the United States Space Force.

“Launch and early operations is always a very stressful period for satellite operators. With Azure Orbital, we achieved contact with MuSat-1 within six minutes of separation from the launch vehicle. This early success, along with our continuous on-orbit operations, gives us confidence to use Azure Orbital for future missions.”
Paige Holland, Operations Automation Lead, Muon Space

Azure Orbital Ground Station for government customers

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Azure Space technologies advance digital transformation across government agencies chevron_right

We’ve seen increasing momentum with commercial customers adopting Azure Orbital Ground Station. Azure Orbital Ground Station is now available in preview within the Microsoft Azure Government region. Introducing Azure Orbital Ground Station into Azure Government enables government customers to fully leverage a global partner ecosystem of ground stations, cloud modems, self-service scheduling, and a managed data path.

True Anomaly and Viasat are leveraging Azure Orbital Ground Station in Azure Government for space domain awareness

True Anomaly selected Microsoft and Viasat to provide ground support for its upcoming launch of two Jackal spacecrafts—autonomous orbital vehicles for rendezvous and proximity operations. True Anomaly will schedule satellite contacts at Viasat Real Time Earth (RTE) sites using Azure Orbital Ground Station in Azure Government.

“Azure Orbital Ground Station’s managed data path makes it easy to connect to a global ground network. With one click of a button on Azure, we gain access to all Viasat Real Time Earth sites and simply indicate where the data from our spacecraft should land, while Microsoft handles the orchestration and connectivity. Working within Azure Government lets us meet our customers where they are.”
Jared Kirkpatrick, Jackal Block 1 Project Manager, True Anomaly

Provisioning fiber to Viasat sites

To provide customers with their data as quickly and securely as possible, Microsoft is provisioning high-speed, real-time cloud connectivity to select Viasat RTE sites, allowing customers to stream multi-gigabit per second downlinks.

“Viasat is collaborating with Microsoft to enable low-touch access to space communication solutions for our customers like True Anomaly. Azure Orbital Ground Station offers a common data plane and API to access our global antenna network that includes very high throughput data downlinks over Ka-band.”
Aaron Hawkins, Real Time Earth Director for Strategic Partnerships, Viasat

Watch this video to learn more about how our customers are using Azure Orbital Ground Support in support of their missions.  

Gaining insights from space data

As the volume and value of space data continues to grow, having easy and affordable access to ground infrastructure will play a central role in serving customers’ mission-critical operations. So too will be the ability of customers to access and analyze near real-time data gathered from space.

The future of the cloud will incorporate space solutions such as satellite connectivity and Earth observational data. Space-based sensors observing Earth and satellite data will increasingly be used to improve our data insights on the ground.

The latest episode in the Microsoft Future of the Cloud Webinar series explores the role of space data in creating “a planetary computer for a sustainable future.”

Watch the series to learn about:

Leveraging the potential of the cloud and space to enable data-driven decision making for your organization and missions.

How Microsoft Planetary Computer supports global efforts of environmental sustainability and Earth science by enabling developers to build tools for measuring, monitoring, modeling, and managing healthy ecosystems.

The potential opportunities that a new Azure Space data solution built on the Microsoft Planetary Computer will create for Microsoft’s customers to unlock the full potential of their Earth observation data.

Empowering developers to build, deploy, and operate on-orbit

Empowering any developer to build and deploy applications into space will be critical to lowering the barrier to entry for participating in the space industry. Azure Orbital’s software development kit provides satellite operators with the tools and capabilities to unlock new business models and enable mission requirements.

Loft Orbital customer onboarding for virtual missions on YAM-6 is now open

Over the past two years, Microsoft and Loft Orbital have been collaborating to lower the barriers to entry for space. A key pillar in this collaboration has been the enablement of “virtual missions,” making it easier for developers to access space capabilities without having to develop or launch their own hardware in space, and instead by simply writing software applications.

YAM-6 is the first satellite fully dedicated to offering this capability. Last week, we announced that YAM-6 is now publicly accepting customers for virtual missions for 2024. General availability is planned for April 2024.

“Our joint product offering leverages Loft’s space infrastructure and Microsoft’s cloud and ground infrastructure to make it simple for anyone to deploy AI applications in space at scale. YAM-6 is supported by the Azure Orbital product portfolio, including Azure Orbital Ground Station, Azure Orbital space edge on-orbit application framework.”
Pierre Damien Voujour, Cofounder and Chief Executive Officer, Loft Orbital

Space Compass leveraging virtual missions to prove out concepts quickly

Space Compass—a joint venture company between NTT, Japanese Information and Communications Technology (ICT) leader, and SKY Perfect JSAT Corporation, Asia’s largest satellite operator—is on a multi-year mission to deploy space-edge computing capabilities together with an ultra-speed optical data relay network. This will allow space data users to utilize real-time data much more efficiently in the cloud environment (see Figure 1).

Over the past three months, Space Compass has been working with Microsoft to explore use cases in an effort to better understand and demonstrate the value of on-orbit processing, and how to shape their future space infrastructure to support it.

“We are very excited to closely collaborate with the Microsoft team to develop a cutting-edge space computing solution. This is one of our key initiatives to realize the Space Integrated Computing Network.”
Shigehiro Hori, Co-Chief Executive Officer, Space Compass

Space Compass will be running a virtual mission on YAM-6 to demonstrate AI-based ship detection. This demonstration paves the way and de-risks future missions that will be flown on Space Compasses’ own satellites. Learn more about the Space Compass mission.

Figure 1: Ship detection program.

Both Microsoft and Space Compass believe in the power of on-orbit processing, bringing AI to the edge in space with high-speed connectivity to the cloud.

Learn More

These exciting use cases are just the beginning. Learn more about the ways that Microsoft Azure Space can transform how you deliver on your mission:

Sign up for news and updates on how Azure Space data can advance your organization and missions, or complete this form to get in touch with the Azure Space team.

Visit the Azure Orbital Ground Station website and documentation page.

The post Create new ways to serve your mission with Microsoft Azure Space appeared first on Azure Blog.
Quelle: 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