Cloud Computing Köln

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.

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Quelle: Azure