The Future of AI: Exploring Microsoft Azure AI Studio's Cutting-Edge Features

Introduction

In the rapidly evolving world of technology, Artificial Intelligence (AI) stands at the forefront of innovation. Among the various platforms driving this revolution, Microsoft Azure AI Studio emerges as a leader, offering a suite of cutting-edge features designed to harness the full potential of AI. In this comprehensive article, we delve into the future of AI, exploring the sophisticated capabilities of Azure AI Studio that set it apart from the competition.

What is Microsoft Azure AI Studio?

Microsoft Azure AI Studio is a robust platform that provides developers and businesses with the tools necessary to build, train, and deploy AI models at scale. Leveraging Azure's powerful cloud infrastructure, it offers unparalleled flexibility, scalability, and integration, making it an essential resource for anyone looking to implement AI solutions.

Key Features of Microsoft Azure AI Studio

1. Automated Machine Learning (AutoML)

Automated Machine Learning (AutoML) is a groundbreaking feature of Azure AI Studio. AutoML simplifies the process of training machine learning models by automating the selection of algorithms, hyperparameters, and pre-processing steps. This feature significantly reduces the time and expertise required to develop high-quality models, allowing data scientists and developers to focus on refining and deploying their AI solutions.

2. Cognitive Services

Cognitive Services in Azure AI Studio offer pre-built APIs that enable developers to add intelligent features to their applications. These services include vision, speech, language, decision, and search capabilities. For instance, the Computer Vision API allows applications to analyze and interpret visual data, while the Speech API enables advanced speech recognition and synthesis.

3. Machine Learning Operations (MLOps)

Machine Learning Operations (MLOps) is a critical component of Azure AI Studio, streamlining the entire machine learning lifecycle. MLOps integrates development (DevOps) and machine learning (ML) processes, ensuring efficient model management, deployment, monitoring, and governance. This integration enhances collaboration between data scientists and IT operations, resulting in more reliable and scalable AI solutions.

4. Responsible AI

Microsoft Azure AI Studio emphasizes the importance of Responsible AI. This initiative focuses on ensuring that AI systems are fair, transparent, and accountable. Azure provides tools and frameworks to help developers assess and mitigate biases, enhance interpretability, and ensure compliance with ethical standards and regulations. This commitment to ethical AI fosters trust and confidence in AI applications.

5. Custom Vision

Custom Vision is a specialized service within Azure AI Studio that allows users to build and deploy custom image classification models. By uploading labeled images, users can train models to recognize specific objects or scenes, making it ideal for applications in manufacturing, retail, healthcare, and more. The intuitive interface and powerful training capabilities of Custom Vision streamline the development of sophisticated image recognition solutions.

6. Azure Machine Learning Designer

The Azure Machine Learning Designer is a drag-and-drop tool that simplifies the creation of machine learning pipelines. With its user-friendly interface, even those with limited coding experience can design, test, and deploy machine learning models. The designer supports a wide range of machine learning tasks, from data preprocessing to model training and evaluation, making it accessible to a broad audience.

The Future of AI with Microsoft Azure AI Studio

1. Enhanced Integration with IoT

The integration of Internet of Things (IoT) with Azure AI Studio is set to revolutionize industries by enabling real-time data analysis and decision-making. Azure IoT Hub, combined with AI capabilities, allows businesses to deploy AI models on edge devices, facilitating predictive maintenance, process optimization, and enhanced operational efficiency.

2. Advanced Natural Language Processing (NLP)

Azure AI Studio continues to advance in the field of Natural Language Processing (NLP). With services like Azure Language Understanding (LUIS) and Text Analytics, developers can create applications that understand and respond to human language more accurately. These advancements are driving innovations in chatbots, virtual assistants, and automated customer service solutions.

3. Quantum Computing Integration

The future of AI on Azure is also closely tied to the development of quantum computing. Azure Quantum offers a comprehensive set of quantum computing services and tools, positioning Microsoft at the forefront of quantum-AI integration. This combination promises to solve complex problems at unprecedented speeds, opening new horizons for AI research and applications.

4. Democratizing AI

Microsoft's vision of democratizing AI ensures that advanced AI capabilities are accessible to all organizations, regardless of size or expertise. Azure AI Studio's user-friendly tools, extensive documentation, and community support empower businesses to leverage AI for innovation and growth. This democratization is fostering a more inclusive AI ecosystem, where diverse perspectives and ideas contribute to technological advancement.

Conclusion

Microsoft Azure AI Studio is paving the way for the future of AI with its comprehensive suite of features and forward-thinking initiatives. From Automated Machine Learning and Cognitive Services to Responsible AI and Quantum Computing, Azure AI Studio provides the tools and resources necessary to build, deploy, and manage sophisticated AI solutions. As we move forward, the integration of AI with IoT, NLP advancements, and the democratization of AI will continue to shape the technological landscape, driving innovation across industries.

For those looking to stay ahead in the AI revolution, Microsoft Azure AI Studio offers a robust platform that meets the diverse needs of modern AI development.

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Microsoft and G42 partner to accelerate AI innovation in UAE and beyond

Microsoft and G42 have announced a strategic partnership aimed at accelerating AI innovation in the United Arab Emirates (UAE) and beyond. This collaboration will leverage Microsoft's extensive experience in cloud computing and AI technologies alongside G42's deep expertise in AI-driven solutions across various industries.

Strategic partnership highlights:

Expansion of partnership between Microsoft and G42 to deliver advanced AI solutions with Microsoft Azure across various industries and markets.

More Info: DP-600: Implementing Analytics Solutions Using Microsoft Fabric

Microsoft will invest $1.5 billion in G42 for a minority stake in G42 and join its board of directors.

Companies will support the establishment of a $1 billion fund for developers to boost AI skills in the United Arab Emirates (UAE) and broader region.

Expanded strategic partnership:

Today, we announced a strategic investment in G42, a leading AI company in the UAE, to co-innovate and deliver advanced AI solutions with Microsoft Azure for various industries and markets across the Middle East, Central Asia and Africa.

Microsoft will invest $1.5 billion in G42 for a minority stake in the company with Brad Smith, Microsoft Vice Chair and President, joining G42’s board of directors — strengthening the long-standing collaboration and mutual synergies between the two companies. With the breadth of the Microsoft Cloud and its differentiated AI capabilities, the deal significantly advances G42’s strategy of delivering generative AI and next-generation infrastructure and services for a range of customers across financial services, healthcare, energy, government and education.

The commercial partnership is backed by assurances to the U.S. and UAE governments through a first-of- its-kind binding agreement to apply world-class best practices to ensure the secure, trusted, and responsible development and deployment of AI. Microsoft and G42 will work closely together to elevate the security and compliance framework of their joint international infrastructure. Both companies will move forward with a commitment to comply with U.S. and international trade, security, responsible AI, and business integrity laws and regulations. The work on these topics is governed by a detailed Intergovernmental Assurance Agreement between G42 and Microsoft that was developed in close consultation with both the UAE and U.S. governments.

Foundational to the partnership is G42’s trust and commitment in Microsoft’s cloud platform. G42 will expand its existing commitment to deploying Microsoft Cloud offerings, demonstrating confidence in Microsoft as its preferred partner to enhance services and deliver value-added solutions to its customers. With the partnership, G42’s data platform and other essential technology infrastructure will migrate to Microsoft Azure to benefit from industry-leading performance, scalability and security capabilities. Migrating to Azure will also support AI product development that allows G42 to create services that can scale to achieve faster delivery times for its customers globally. Together, we look forward to accelerating AI transformation in emerging markets and advancing equitable growth in AI globally.

Building on our technical co-innovation

G42 brings an excellent track record as a leader actively driving global progress and accessibility in AI technologies, and Microsoft and G42 have worked closely together to help optimize Cloud and AI solutions for the Middle East.

Last year, G42 was one of the first partners to commit to implementing Microsoft Cloud for Sovereignty offering to UAE-based organizations. G42 is helping public sector and regulated industries to use new platform capabilities for securing sensitive data, providing access to the latest cloud and AI features available on Azure public cloud, and ensuring they comply with local privacy and regulatory requirements. G42’s deep understanding of UAE sovereignty requirements and technical capabilities are central to customizing Microsoft Cloud for Sovereignty to help address customer’s specific needs.

Microsoft also announced that Jais, G42’s Arabic Large Language Model (LLM), will be available in the Azure AI Model Catalog. This model represents a significant advancement for the Arabic world in AI, offering over 400 million Arabic speakers the opportunity to explore the potential of generative AI. Jais is the world’s first Arabic LLM developed by G42 in collaboration with Cerebras, Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), and Med42 LLM, a generative AI model to streamline medical reporting. The expanded partnership with Microsoft will help accelerate the adoption of G42’s groundbreaking AI products and services, such as Jais, making them available through Microsoft Azure.

Announced in March of this year, First Abu Dhabi Bank (FAB), the UAE’s largest bank, will collaborate with Core42, a subsidiary of G42, to accelerate its digital transformation journey leveraging Microsoft Azure trusted cloud platform for enterprises. FAB will move its datacenter and workload to Azure, enabling the bank to use Core42’s sovereign controls platform, which is built on Azure and ensures the highest standards of data sovereignty and compliance with UAE regulations.

One of the leading examples of precision medicine in action is the collaboration between G42 subsidiary M42, a global health care company, the Broad Institute of MIT and Harvard, Microsoft, and the International Center for Genetic Disease (ICGD). The partners are using Terra, a scalable and secure platform for biomedical research, to enable data sharing and analysis across different institutions and countries. Terra, powered by Microsoft Azure, allows researchers to access and analyze anonymized genomic data from the Emirati Genome Program, which has completed over 500,000 whole genome sequences to date. By applying AI technologies to this rich data source, the collaborators aim to advance clinical genomic research and disease prevention, as well as support precision medicine and life science strategies globally.

Accelerating access to digital innovation in UAE and the region

Along with providing advanced AI capabilities, the partnership will benefit regions beyond the UAE in ways that will improve how enterprises experience cloud computing. By bringing expanded low latency datacenter infrastructure to emerging markets, Microsoft and G42 will help accelerate digital transformation across key industries in those regions. This will provide countries across the Middle East, Central Asia and Africa with expanded access to services and technologies that will allow them to address the most challenging business concerns while ensuring the highest standards of security and privacy.

Furthermore, the partnership will also support the development of a skilled and diverse AI workforce and talent pool that will drive innovation and competitiveness for the UAE and broader region with the investment of $1 billion in a fund for developers.

Source: microsoft.com

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From code to production: New ways Azure helps you build transformational AI experiences

We’re witnessing a critical turning point in the market as AI moves from the drawing boards of innovation into the concrete realities of everyday life. The leap from potential to practical application marks a pivotal chapter, and you, as developers, are key to bringing it to bear.

The news at Build is focused on the top demands we’ve heard from all of you as we’ve worked together to turn this promise of AI into reality:

• Empowering every developer to move with greater speed and efficiency, using the tools you already know and love.
• Expanding and simplifying access to the AI, data—application platform services you need to be successful so you can focus on building transformational AI experiences.
• And, helping you focus on what you do best—building incredible applications—with responsibility, safety, security, and reliability features, built right into the platform. 

I’ve been building software products for more than two decades now, and I can honestly say there’s never been a more exciting time to be a developer. What was once a distant promise is now manifesting—and not only through the type of apps that are possible, but how you can build them.

With Microsoft Azure, we’re meeting you where you are today—and paving the way to where you’re going. So let’s jump right into some of what you’ll learn over the next few days. Welcome to Microsoft Build 2024!

Create the future with Azure AI: offering you tools, model choice, and flexibility  

The number of companies turning to Azure AI continues to grow as the list of what’s possible expands. We’re helping more than 50,000 companies around the globe achieve real business impact using it—organizations like Mercedes-Benz, Unity, Vodafone, H&R Block, PwC, SWECO, and so many others.  

To make it even more valuable, we continue to expand the range of models available to you and simplify the process for you to find the right models for the apps you’re building.

Azure AI Studio, a key component of the copilot stack, is now generally available. The pro-code platform empowers responsible generative AI development, including the development of your own custom copilot applications. The seamless development approach includes a friendly user interface (UI) and code-first capabilities, including Azure Developer CLI (AZD) and AI Toolkit for VS Code, enabling developers to choose the most accessible workflow for their projects.

Developers can use Azure AI Studio to explore AI tools, orchestrate multiple interoperating APIs and models; ground models using their data using retrieval augmented generation (RAG) techniques; test and evaluate models for performance and safety; and deploy at scale and with continuous monitoring in production.

Empowering you with a broad selection of small and large language models  

Our model catalog is the heart of Azure AI Studio. With more than 1,600 models available, we continue to innovate and partner broadly to bring you the best selection of frontier and open large language models (LLMs) and small language models (SLMs) so you have flexibility to compare benchmarks and select models based on what your business needs. And, we’re making it easier for you to find the best model for your use case by comparing model benchmarks, like accuracy and relevance.

I’m excited to announce OpenAI’s latest flagship model, GPT-4o, is now generally available in Azure OpenAI Service. This groundbreaking multimodal model integrates text, image, and audio processing in a single model and sets a new standard for generative and conversational AI experiences. Pricing for GPT-4o is $5/1M Tokens for input and $15/1M Tokens for output.

Earlier this month, we enabled GPT-4 Turbo with Vision through Azure OpenAI Service. With these new models developers can build apps with inputs and outputs that span across text, images, and more, for a richer user experience. 

We’re announcing new models through Models-as-a-Service (MaaS) in Azure AI Studio leading Arabic language model Core42 JAIS and TimeGen-1 from Nixtla are now available in preview. Models from AI21, Bria AI, Gretel Labs, NTT DATA, Stability AI as well as Cohere Rerank are coming soon.  

Phi-3: Redefining what’s possible with SLMs

At Build we’re announcing Phi-3-small, Phi-3-medium, and Phi-3-vision, a new multimodal model, in the Phi-3 family of AI small language models (SLMs), developed by Microsoft. Phi-3 models are powerful, cost-effective and optimized for resource constrained environments including on-device, edge, offline inference, and latency bound scenarios where fast response times are critical. 

Sized at 4.2 billion parameters, Phi-3-vision supports general visual reasoning tasks and chart/graph/table reasoning. The model offers the ability to input images and text, and to output text responses. For example, users can ask questions about a chart or ask an open-ended question about specific images. Phi-3-mini and Phi-3-medium are also now generally available as part of Azure AI’s MaaS offering.

In addition to new models, we are adding new capabilities across APIs to enable multimodal experiences. Azure AI Speech has several new features in preview including Speech analytics and Video translation to help developers build high-quality, voice-enabled apps. Azure AI Search now has dramatically increased storage capacity and up to 12X increase in vector index size at no additional cost to run RAG workloads at scale.

Bring your intelligent apps and ideas to life with Visual Studio, GitHub, and the Azure platform

The tools you choose to build with should make it easy to go from idea to code to production. They should adapt to where and how you work, not the other way around. We’re sharing several updates to our developer and app platforms that do just that, making it easier for all developers to build on Azure. 

Access Azure services within your favorite tools for faster app development

By extending Azure services natively into the tools and environments you’re already familiar with, you can more easily build and be confident in the performance, scale, and security of your apps.  

We’re also making it incredibly easy for you to interact with Azure services from where you’re most comfortable: a favorite dev tool like VS Code, or even directly on GitHub, regardless of previous Azure experience or knowledge. Today, we’re announcing the preview of GitHub Copilot for Azure, extending GitHub Copilot to increase its usefulness for all developers. You’ll see other examples of this from Microsoft and some of the most innovative ISVs at Build, so be sure to explore our sessions.  

Also in preview today is the AI Toolkit for Visual Studio Code, an extension that provides development tools and models to help developers acquire and run models, fine-tune them locally, and deploy to Azure AI Studio, all from VS Code.  

Updates that make cloud native development faster and easier

.NET Aspire has arrived! This new cloud-native stack simplifies development by automating configurations and integrating resilient patterns. With .NET Aspire, you can focus more on coding and less on setup while still using your preferred tools. This stack includes a developer dashboard for enhanced observability and diagnostics right from the start for faster and more reliable app development. Explore more about the general availability of .NET Aspire on the DevBlogs post.   

We’re also raising the bar on ease of use in our application platform services, introducing Azure Kubernetes Services (AKS) Automatic, the easiest managed Kubernetes experience to take AI apps to production. In preview now, AKS Automatic builds on our expertise running some of the largest and most advanced Kubernetes applications in the world, from Microsoft Teams to Bing, XBox online services, Microsoft 365 and GitHub Copilot to create best practices that automate everything from cluster set up and management to performance and security safeguards and policies.

As a developer you now have access to a self-service app platform that can move from container image to deployed app in minutes while still giving you the power of accessing the Kubernetes API. With AKS Automatic you can focus on building great code, knowing that your app will be running securely with the scale, performance and reliability it needs to support your business.

Data solutions built for the era of AI

Developers are at the forefront of a pivotal shift in application strategy which necessitates optimizations at every tier of an application—including databases—since AI apps require fast and frequent iterations to keep pace with AI model innovation. 

We’re excited to unveil new data and analytics features this week designed to assist you in the critical aspects of crafting intelligent applications and empowering you to create the transformative apps of today and tomorrow.

Enabling developers to build faster with AI built into Azure databases 

Vector search is core to any AI application so we’re adding native capabilities to Azure Cosmos DB with Azure Cosmos DB for NoSQL. Powered by DiskANN, a powerful algorithm library, this makes Azure Cosmos DB the first cloud database to offer lower latency vector search at cloud scale without the need to manage servers. 

We’re also announcing the availability of Azure Database for PostgreSQL extension for Azure AI to make bringing AI capabilities to data in PostgreSQL data even easier. Now generally available, this enables developers who prefer PostgreSQL to plug data directly into Azure AI for a simplified path to leverage LLMs and build rich PostgreSQL generative AI experiences.   

Embeddings enable AI models to better understand relationships and similarities between data, which is key for intelligent apps. Azure Database for PostgreSQL in-database embedding generation is now in preview so embeddings can be generated right within the database—offering single-digit millisecond latency, predictable costs, and the confidence that data will remain compliant for confidential workloads. 

Making developer life easier through in-database Copilot capabilities

These databases are not only helping you build your own AI experiences. We’re also applying AI directly in the user experience so it’s easier than ever to explore what’s included in a database. Now in preview, Microsoft Copilot capabilities in Azure SQL DB convert queries into SQL language so developers can use natural language to interact with data. And, Copilot capabilities are coming to Azure Database for MySQL to provide summaries of technical documentation in response to user questions—creating an all-around easier and more enjoyable management experience.

Microsoft Copilot capabilities in the database user experience

Microsoft Fabric updates: Build powerful solutions securely and with ease

We have several Fabric updates this week, including the introduction of Real-Time Intelligence. This completely redesigned workload enables you to analyze, explore, and act on your data in real time. Also coming at Build: the Workload Development Kit in preview, making it even easier to design and build apps in Fabric. And our Snowflake partnership expands with support for Iceberg data format and bi-directional read and write between Snowflake and Fabric’s OneLake.

Spend a day in the life of a piece of data and learn exactly how it moves from its database home to do more than ever before with the insights of Microsoft Fabric, real-time assistance by Microsoft Copilot, and the innovative power of Azure AI.  

Build on a foundation of safe and responsible AI

What began with our principles and a firm belief that AI must be used responsibly and safely has become an integral part of the tooling, APIs, and software you use to scale AI responsibly. Within Azure AI, we have 20 Responsible AI tools with more than 90 features. And there’s more to come, starting with updates at Build.

New Azure AI Content Safety capabilities

We’re equipping you with advanced guardrails that help protect AI applications and users from harmful content and security risks and this week, we’re announcing new  feature for Azure AI Content Safety. Custom Categories are coming soon so you can create custom filters for specific content filtering needs. This feature also includes a rapid option, enabling you to deploy new custom filters within an hour to protect against emerging threats and incidents.  

Prompt Shields and Groundedness Detection are both available in preview now in Azure OpenAI Service and Azure AI Studio help fortify AI safety. Prompt shields mitigate both indirect and jailbreak prompt injection attacks on LLMs, while Groundedness Detection enables detection of ungrounded materials or hallucinations in generated responses.  

Features to help secure and govern your apps and data

Microsoft Defender for Cloud now extends its cloud-native application protection to AI applications from code to cloud. And, AI security posture management capabilities enable security teams to discover their AI services and tools, identify vulnerabilities, and proactively remediate risks. Threat protection for AI workloads in Defender for Cloud leverages a native integration with Azure AI Content Safety to enable security teams to monitor their Azure OpenAl applications for direct and in-direct prompt injection attacks, sensitive data leaks and other threats so they can quickly investigate and respond.

With easy-to-use APIs, app developers can easily integrate Microsoft Purview into line of business apps to get industry-leading data security and compliance for custom-built AI apps. You can empower your app customers and respective end users to discover data risks in AI interactions, protect sensitive data with encryption, and govern AI activities. These capabilities are available for Copilot Studio in public preview and soon (coming in July) will be available in public preview for Azure AI Studio, and via the Purview SDK, so developers can benefit from the data security and compliance controls for their AI apps built on Azure AI.

Two final security notes. We’re also announcing a partnership with HiddenLayer to scan open models that we onboard to the catalog, so you can verify that the models are free from malicious code and signs of tampering before you deploy them. We are the first major AI development platform to provide this type of verification to help you feel more confident in your model choice. 

Second, Facial Liveness, a feature of the Azure AI Vision Face API which has been used by Windows Hello for Business for nearly a decade, is now available in preview for browser. Facial Liveness is a key element in multi-factor authentication (MFA) to prevent spoofing attacks, for example, when someone holds a picture up to the camera to thwart facial recognition systems. Developers can now easily add liveness and optional verification to web applications using Face Liveness, with the Azure AI Vision SDK, in preview.

Our belief in the safe and responsible use of AI is unwavering. You can read our recently published Responsible AI Transparency Report for a detailed look at Microsoft’s approach to developing AI responsibly. We’ll continue to deliver more innovation here and our approach will remain firmly rooted in principles and put into action with built-in features.

Move your ideas from a spark to production with Azure

Organizations are rapidly moving beyond AI ideation and into production. We see and hear fresh examples every day of how our customers are unlocking business challenges that have plagued industries for decades, jump-starting the creative process, making it easier to serve their own customers, or even securing a new competitive edge. We’re curating an industry-leading set of developer tools and AI capabilities to help you, as developers, create and deliver the transformational experiences that make this all possible.

Source: microsoft.com

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Unleashing innovation: The new era of compute powering Azure AI solutions

As AI continues to transform industries, Microsoft is expanding its global cloud infrastructure to meet the needs of developers and customers everywhere. At Microsoft Build 2024, we’re unveiling our latest progress in developing tools and services optimized for powering your AI solutions. Microsoft’s cloud infrastructure is unique in how it provides choice and flexibility in performance and power for customers’ unique AI needs, whether that’s doubling deployment speeds or lowering operating costs.

That’s why we’ve enhanced our adaptive, powerful, and trusted platform with the performance and resilience you’ll need to build intelligent AI applications. We’re delivering on our promise to support our customers by providing them with exceptional cost-performance in compute and advanced generative AI capabilities.

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Powerful compute for general purpose and AI workloads

Microsoft has the expertise and scale to run the AI supercomputers that power some of the world’s biggest AI services, such as Microsoft Azure OpenAI Service, ChatGPT, Bing, and more. Our focus as we continue to expand our AI infrastructure is on optimizing performance, scalability, and power efficiency.

Microsoft takes a systems approach to cloud infrastructure, optimizing both hardware and software to efficiently handle workloads at scale. In November 2023, Microsoft introduced its first in-house designed cloud compute processor, Azure Cobalt 100, which enables general-purpose workloads on the Microsoft Cloud. We are announcing the preview of Azure virtual machines built to run on Cobalt 100 processors. Cobalt 100-based virtual machines (VMs) are Azure’s most power efficient compute offering, and deliver up to 40% better performance than our previous generation of Arm-based VMs. And we’re delivering that same Arm-based performance and efficiency to customers like Elastic, MongoDB, Siemens, Snowflake, and Teradata. The new Cobalt 100-based VMS are expected to enhance efficiency and performance for both Azure customers and Microsoft products. Additionally, IC3, the platform that powers billions of customer conversations in Microsoft Teams, is adopting Cobalt 100 to serve its growing customer base more efficiently, achieving up to 45% better performance on Cobalt 100 VMs.

We’re combining the best of industry and the best of Microsoft in our AI infrastructure. Alongside our custom Azure Cobalt 100 and Maia series and silicon industry partnerships, we’re also announcing the general availability of the ND MI300X VM series, where Microsoft is the first cloud provider to bring AMD’s most powerful Instinct MI300X Accelerator to Azure. With the addition of the ND MI300X VM combining eight AMD MI300X Instinct accelerators, Azure is delivering customers unprecedented cost-performance for inferencing scenarios of frontier models like GPT-4. Our infrastructure supports different scenarios for AI supercomputing, such as building large models from scratch, running inference on pre-trained models, using model as a service providers, and fine-tuning models for specific domains.

One of Microsoft’s advantages in AI is our ability to combine thousands of virtual machines with tens of thousands of GPUs with the best of InfiniBand and Ethernet based networking topologies for supercomputers in the cloud that can run large scale AI workloads to lower costs. With a diversity of silicon across AMD, NVIDIA, and Microsoft’s Maia AI accelerators, Azure’s AI infrastructure delivers the most complete compute platform for AI workloads. It is this combination of advanced AI accelerators, datacenter designs, and optimized compute and networking topology that drive cost efficiency per workload. That means whether you use Microsoft Copilot or build your own copilot apps, the Azure platform ensures you get the best AI performance with optimized cost.

Microsoft is further extending our cloud infrastructure with the Azure Compute Fleet, a new service that simplifies provisioning of Azure compute capacity across different VM types, availability zones, and pricing models to more easily achieve desired scale, performance, and cost by enabling users to control VM group behaviors automatically and programmatically. As a result, Compute Fleet has the potential to greatly optimize your operational efficiency and increase your core compute flexibility and reliability for both AI and general-purpose workloads together at scale.

AI-enhanced central management and security

As businesses continue to expand their computing estate, managing and governing the entire infrastructure can become overwhelming. We keep hearing from developers and customers that they spend more time searching for information and are less productive. Microsoft is focused on simplifying this process through AI-enhanced central management and security. Our adaptive cloud approach takes innovation to the next level with a single, intelligent control plane that spans from cloud to edge, making it easier for customers to manage their entire computing estate in a consistent way. We’re also aiming to improve your experience with managing these distributed environments through Microsoft Copilot in Azure.

We created Microsoft Copilot in Azure to act as an AI companion, helping your teams manage operations seamlessly across both cloud and edge environments. By using natural language, you can ask Copilot questions and receive personalized recommendations related to Azure services. Simply ask, “Why is my app slow?” or “How do I fix this error?” and Copilot will navigate a customer through potential causes and fixes.

Starting today, we will be opening the preview of Copilot in Azure to all customers over the next couple of weeks. With this update, customers can choose to have all their users access Copilot or grant access to specific users or groups within a tenant. With this flexibility to manage Copilot, you can tailor your approach and control which groups of users or departments within your organization have access to it. You can feel secure knowing you can deploy and use the tool in a controlled manner, ensuring it aligns with your organization’s operational standards and security policies.

We’re continually enhancing Copilot and making the product better with every release to help developers be more productive. One of the ways we’ve simplified the developer’s experience is by making databases and analytics services easier to configure, manage, and optimize through AI-enhanced management. Several new skills are available for Azure Kubernetes Service (AKS) in Copilot for Azure that simplify common management tasks, including the ability to configure AKS backups, change tiers, locate YAML files for editing, and construct kubectl commands.

We’ve also added natural language to SQL conversion and self-help for database administration to support your Azure SQL database-driven applications. Developers can ask questions about their data in plain text, and Copilot generates the corresponding T-SQL query. Database administrators can independently manage databases, resolve issues, and learn more about performance and capabilities. Developers benefit from detailed explanations of the generated queries, helping them write code faster.

Lastly, you’ll notice a few new security enhancements to the tool. Copilot now includes Microsoft Defender for Cloud prompting capabilities to streamline risk exploration, remediation, and code fixes. Defender External Attack Surface Management (EASM) leverages Copilot to help surface risk-related insights and convert natural language to corresponding inventory queries across data discovered by Defender EASM. These features make database queries more user-friendly, enabling our customers to use natural language for any related queries. We’ll continue to expand Copilot capabilities in Azure so you can be more productive and focused on writing code.

Cloud infrastructure built for limitless innovation

Microsoft is committed to helping you stay ahead in this new era by giving you the power, flexibility, and performance you need to achieve your AI ambitions. Our unique approach to cloud and AI infrastructure helps us and developers like you meet the challenges of the ever-changing technological landscape head-on so you can continue working efficiently while innovating at scale.

Source: microsoft.com

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3 ways Microsoft Azure AI Studio helps accelerate the AI development journey

The generative AI revolution is here, and businesses across the globe and across industries are adopting the technology into their work. However, the learning curve for your own AI applications can be steep, with 52% of organizations reporting that a lack of skilled workers is their biggest barrier to implement and scale AI. To reap the true value of generative AI, organizations need tools to simplify AI development, so they can focus on the big picture of solving business needs. Microsoft Azure AI Studio, Microsoft’s generative AI platform, is designed to democratize the AI development process for developers, bringing together the models, tools, services, and integrations you need to get started developing your own AI application quickly.  

“Azure AI Studio improved the experience for creating AI products. We found it mapped perfectly to our needs for faster development and time to market, and greater throughput, scalability, security, and trust.” 

Denis Yarats, Chief Technology Officer and Cofounder, Perplexity.AI 

1. Develop how you want   

The Azure AI Studio comprehensive user interface (UI) and code-first experiences empower developers to choose their preferred method of working, whether it’s through a user-friendly, accessible interface or by diving directly into code. This flexibility is crucial for rapid project initiation, iteration, and collaboration—allowing teams to work in the manner that best suits their skills and project requirements.  

The choice for where to develop was important for IWill Therapy and IWill CARE, a leading online mental health care provider in India, when they started using Azure AI Studio to build a solution to reach more clients. IWill created a Hindi-speaking chatbot named IWill GITA using the cutting-edge products and services included in the Azure AI Studio platform. IWill‘s scalable, AI-powered copilot brings mental health access and therapist-like conversations to people throughout India.

The comprehensible UI in Azure AI Studio made it easy for cross functional teams to get on the same page, allowing workers with less AI development experience to skill up quickly.  

“We found that the Azure user interface removed the communication gap between engineers and businesspeople. It made it easy for us to train subject-matter experts in one day”. 

Ashish Dwivedi, Co-founder and COO, iWill Therapy

Azure AI Studio allows developers to move seamlessly between its friendly user interface and code, with software development kits (SDKs) and Microsoft Visual Studio code extensions for local development experiences. The Azure AI Studio dual approach caters to diverse development preferences, streamlining the process from exploration to deployment, ultimately enabling developers to bring their AI projects to life more quickly and effectively. 

2. Identify the best model for your needs

The Azure AI Studio model catalog offers a comprehensive hub for discovering, evaluating, and consuming foundation models, including a wide array of leading models from Meta, Mistral, Hugging Face, OpenAI, Cohere, Nixtla, G42 Jais, and many more. To enable developers to make an informed decision about which model to use, Azure AI Studio offers tools such as model benchmarking. With model benchmarking, developers can quickly compare models by task using open-source datasets and industry-standard metrics, such as accuracy and fluency. Developers can also explore model cards that detail model capabilities and limitations and try sample inferences to ensure the model is a good fit. 

The Azure AI Studio integration of models from leading partners is already helping customers streamline their development process and accelerating the time to market for their AI solutions. When Perplexity.AI was building their own copilot, a conversational answer engine named Perplexity Ask, Azure AI Studio enabled them to explore various models and to choose the best fit for their solution.  

“Trying out large language models available with Azure OpenAI Service was easy, with just a few clicks to get going. That’s an important differentiator of Azure AI Studio: we had our first prototype in hours. We had more time to try more things, even with our minimal headcount.”  

Denis Yarats, CTO and Cofounder, Perplexity.AI 

3. Streamline your development cycles

Prompt flow in Azure AI Studio is a powerful feature that streamlines the development cycle of generative AI solutions. Developers can develop, test, evaluate, debug, and manage large language model (LLM) flows. You can now monitor their performance, including quality and operational metrics, in real-time, and optimize your flows as needed. Prompt flow is designed to be effortless, with a visual graph for easy orchestration, and integrations with open-source frameworks like LangChain and Semantic Kernel. Prompt flow also facilitates collaboration across teams; multiple users can work together on prompt engineering projects, share LLM assets, evaluate quality and safety of flows, maintain version control, and automate workflows for streamlined large language model operations (LLMOps). 

When Siemens Digital Industries Software wanted to build a solution for its customers and frontline work teams to communicate with operations and engineering teams in real-time to better drive innovation and rapidly address problems as they arise, they looked to Azure AI Studio to create their own copilot. Siemens developers combined Microsoft Teams capabilities with Azure AI Studio and its comprehensive suite of tools, including prompt flow, to streamline workflows that included prototyping, deployment, and production. 

“Our developers really like the UI-first approach of prompt flow and the ease of Azure AI Studio. It definitely accelerated our adoption of advanced machine learning technologies, and they have a lot of confidence now for ongoing AI innovation with this solution and others to come.”  

Manal Dave, Advanced Software Engineer, Siemens Digital Industries Software

Source: microsoft.com

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Introducing GPT-4o: OpenAI’s new flagship multimodal model now in preview on Azure

Microsoft is thrilled to announce the launch of GPT-4o, OpenAI’s new flagship model on Azure AI. This groundbreaking multimodal model integrates text, vision, and audio capabilities, setting a new standard for generative and conversational AI experiences. GPT-4o is available now in Azure OpenAI Service, to try in preview, with support for text and image.

A step forward in generative AI for Azure OpenAI Service

GPT-4o offers a shift in how AI models interact with multimodal inputs. By seamlessly combining text, images, and audio, GPT-4o provides a richer, more engaging user experience.

Launch highlights: Immediate access and what you can expect

Azure OpenAI Service customers can explore GPT-4o’s extensive capabilities through a preview playground in Azure OpenAI Studio starting today in two regions in the US. This initial release focuses on text and vision inputs to provide a glimpse into the model’s potential, paving the way for further capabilities like audio and video.

Efficiency and cost-effectiveness

GPT-4o is engineered for speed and efficiency. Its advanced ability to handle complex queries with minimal resources can translate into cost savings and performance.

Potential use cases to explore with GPT-4o

The introduction of GPT-4o opens numerous possibilities for businesses in various sectors: 

1. Enhanced customer service: By integrating diverse data inputs, GPT-4o enables more dynamic and comprehensive customer support interactions.
2. Advanced analytics: Leverage GPT-4o’s capability to process and analyze different types of data to enhance decision-making and uncover deeper insights.
3. Content innovation: Use GPT-4o’s generative capabilities to create engaging and diverse content formats, catering to a broad range of consumer preferences.

Exciting future developments: GPT-4o at Microsoft Build 2024 

We are eager to share more about GPT-4o and other Azure AI updates at Microsoft Build 2024, to help developers further unlock the power of generative AI.

Source: microsoft.com

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Microsoft is a Leader in the 2024 Gartner Magic Quadrant for Cloud AI Developer Services

We are excited to announce that Microsoft has been named a Leader for the fifth year in a row in the Gartner® Magic Quadrant™ for Cloud AI Developer Services and are especially proud to be placed furthest for our Completeness of Vision.

We’re pleased by the recognition from Gartner as we continue to prioritize investments across our Azure AI portfolio. We’re at the forefront of empowering customers on their generative AI journey—offering a feature rich, unified platform that provides cutting-edge models, services, and fully integrated tooling to accelerate innovation. It’s why over 65% of the Fortune 500 now use Azure OpenAI Service, and tens of thousands of other organizations across industries and around the world are innovating with Azure AI.

Cutting-edge APIs and models 

Azure AI has continued to push the boundaries of innovation, providing customers with cutting-edge APIs and models that are transforming industries and empowering businesses to achieve more. Our Azure AI services are feature rich and bring the capabilities for responsible AI solutions to create, receive, and respond with images, videos, and audio—enabling more natural interactions with technology, and expanding the possibilities for generative AI applications.    

Azure AI’s model catalog is a testament to Microsoft’s commitment to bringing the most advanced AI models to our customers, fostering an environment of innovation and growth. Through industry leaders like OpenAI, Mistral AI, Cohere, Hugging Face, Meta, and more, the model catalog makes available a diverse selection of over 1,600 large language, small language, and vision models accelerating the availability and diversity of AI models for customers, allowing them to choose the best performance and cost options for their applications.  

Albert Heijn, the leading supermarket chain in the Netherlands, is using Azure AI’s cutting-edge APIs and models to power a series of initiatives covering use cases from customer personalization to demand forecast and food waste projects. One of these applications is “Scan my Recipe,” powered by Microsoft Azure AI Vision and Azure OpenAI Service, the tool allows users to conveniently add all the ingredients from a cookbook recipe to their shopping cart, making healthy cooking more accessible.  

“Because we rely heavily on Azure OpenAI and other Azure services for the use of generative AI, we are co-creating with Microsoft and exploring all the technological possibilities together.” 

Noortje van Genugten, Vice President, Product Operations, Albert Heijn 

Fully integrated tooling 

In the fast-paced realm of AI development, developers need a comprehensive ecosystem of tools that support the development lifecycle, from concept to production and ongoing monitoring. Azure AI’s integrated tooling brings together all the tools you need for building your generative AI solution. Retrieval augmented generation, powered by Azure AI Search, enhances AI models by grounding them securely on your chosen data sources, allowing for more contextually relevant and accurate outputs. Fine-tuning further refines these models with specific data, improving their performance on tasks. Prompt flow orchestrates the development process, enabling efficient experimentation and iteration, while model evaluations offer a robust mechanism to assess and improve model performance systematically. Azure AI Content Safety includes a set of customizable filters for both human- and AI-generated content to help ensure safe generative AI experiences for employees, customers, and partners—free of harmful content. Together, these tools streamline the path from AI conception to deployment, enabling rapid innovation and providing the tools to safeguard your applications across the generative AI lifecycle. 

Telstra, Australia’s leading telecommunications and technology company, is using retrieval augmented generation, powered by Azure AI Search, to ground their generative AI solutions securely on their own data. Their new solutions help frontline workers and customer service agents respond to customers more quickly and effectively with Ask Telstra, which puts technical information at the agents’ fingertips, and One Sentence Summary, which instantly brings agents up-to-speed on customer history.  

“90% of customer service agents who tested One Sentence Summary increased their effectiveness. Their calls required 20% less follow-up….Ask Telstra was judged by 84% of the agents using it to positively impact customer interactions.” 

Rohit Lakhotia, General Manager of Customer and Channel AI, Telstra 

Unified AI development platform  

Today’s generative AI solutions, like custom copilots, require various APIs, models, development tooling, responsible AI features, monitoring, and governance. Stitching together disparate products can be a roadblock, and a drain on developer’s time. That’s why we’re bringing together all the needed services and tools for enterprise generative AI, simplifying the creation, testing, and deployment of AI applications, with our investment in Azure AI Studio.   

Azure AI Studio emphasizes a code-first approach, enabling developers to swiftly move from concept to production while adhering to responsible AI practices. It’s a collaborative space that streamlines the development lifecycle, ensuring security, privacy, and compliance are at the forefront of innovation. 

Together, these tools form a robust ecosystem within Azure AI Studio, empowering customers to develop transformative AI-powered applications with confidence and agility.

Source: microsoft.com

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Introducing Phi-3: Redefining what’s possible with SLMs

We are excited to introduce Phi-3, a family of open AI models developed by Microsoft. Phi-3 models are the most capable and cost-effective small language models (SLMs) available, outperforming models of the same size and next size up across a variety of language, reasoning, coding, and math benchmarks. This release expands the selection of high-quality models for customers, offering more practical choices as they compose and build generative AI applications.

Starting today, Phi-3-mini, a 3.8B language model is available on Microsoft Azure AI Studio, Hugging Face, and Ollama. 

• Phi-3-mini is available in two context-length variants—4K and 128K tokens. It is the first model in its class to support a context window of up to 128K tokens, with little impact on quality.
• It is instruction-tuned, meaning that it’s trained to follow different types of instructions reflecting how people normally communicate. This ensures the model is ready to use out-of-the-box.
• It is available on Azure AI to take advantage of the deploy-eval-finetune toolchain, and is available on Ollama for developers to run locally on their laptops.
• It has been optimized for ONNX Runtime with support for Windows DirectML along with cross-platform support across graphics processing unit (GPU), CPU, and even mobile hardware.
• It is also available as an NVIDIA NIM microservice with a standard API interface that can be deployed anywhere. And has been optimized for NVIDIA GPUs. 

In the coming weeks, additional models will be added to Phi-3 family to offer customers even more flexibility across the quality-cost curve. Phi-3-small (7B) and Phi-3-medium (14B) will be available in the Azure AI model catalog and other model gardens shortly.   

Microsoft continues to offer the best models across the quality-cost curve and today’s Phi-3 release expands the selection of models with state-of-the-art small models.

Groundbreaking performance at a small size 

Phi-3 models significantly outperform language models of the same and larger sizes on key benchmarks (see benchmark numbers below, higher is better). Phi-3-mini does better than models twice its size, and Phi-3-small and Phi-3-medium outperform much larger models, including GPT-3.5T.  

All reported numbers are produced with the same pipeline to ensure that the numbers are comparable. As a result, these numbers may differ from other published numbers due to slight differences in the evaluation methodology.

Note: Phi-3 models do not perform as well on factual knowledge benchmarks (such as TriviaQA) as the smaller model size results in less capacity to retain facts. 

Safety-first model design 

Phi-3 models were developed in accordance with the Microsoft Responsible AI Standard, which is a company-wide set of requirements based on the following six principles: accountability, transparency, fairness, reliability and safety, privacy and security, and inclusiveness. Phi-3 models underwent rigorous safety measurement and evaluation, red-teaming, sensitive use review, and adherence to security guidance to help ensure that these models are responsibly developed, tested, and deployed in alignment with Microsoft’s standards and best practices.  

Building on our prior work with Phi models (“Textbooks Are All You Need”), Phi-3 models are also trained using high-quality data. They were further improved with extensive safety post-training, including reinforcement learning from human feedback (RLHF), automated testing and evaluations across dozens of harm categories, and manual red-teaming. Our approach to safety training and evaluations are detailed in our technical paper, and we outline recommended uses and limitations in the model cards.

Unlocking new capabilities 

Microsoft’s experience shipping copilots and enabling customers to transform their businesses with generative AI using Azure AI has highlighted the growing need for different-size models across the quality-cost curve for different tasks. Small language models, like Phi-3, are especially great for: 

• Resource constrained environments including on-device and offline inference scenarios.
• Latency bound scenarios where fast response times are critical.
• Cost constrained use cases, particularly those with simpler tasks.

Thanks to their smaller size, Phi-3 models can be used in compute-limited inference environments. Phi-3-mini, in particular, can be used on-device, especially when further optimized with ONNX Runtime for cross-platform availability. The smaller size of Phi-3 models also makes fine-tuning or customization easier and more affordable. In addition, their lower computational needs make them a lower cost option with much better latency. The longer context window enables taking in and reasoning over large text content—documents, web pages, code, and more. Phi-3-mini demonstrates strong reasoning and logic capabilities, making it a good candidate for analytical tasks. 

Customers are already building solutions with Phi-3. One example where Phi-3 is already demonstrating value is in agriculture, where internet might not be readily accessible. Powerful small models like Phi-3 along with Microsoft copilot templates are available to farmers at the point of need and provide the additional benefit of running at reduced cost, making AI technologies even more accessible.  

ITC, a leading business conglomerate based in India, is leveraging Phi-3 as part of their continued collaboration with Microsoft on the copilot for Krishi Mitra, a farmer-facing app that reaches over a million farmers.

“Our goal with the Krishi Mitra copilot is to improve efficiency while maintaining the accuracy of a large language model. We are excited to partner with Microsoft on using fine-tuned versions of Phi-3 to meet both our goals—efficiency and accuracy!”   

Saif Naik, Head of Technology, ITCMAARS

Originating in Microsoft Research, Phi models have been broadly used, with Phi-2 downloaded over 2 million times. The Phi series of models have achieved remarkable performance with strategic data curation and innovative scaling. Starting with Phi-1, a model used for Python coding, to Phi-1.5, enhancing reasoning and understanding, and then to Phi-2, a 2.7 billion-parameter model outperforming those up to 25 times its size in language comprehension. Each iteration has leveraged high-quality training data and knowledge transfer techniques to challenge conventional scaling laws.

Source: microsoft.com

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Azure Maia for the era of AI: From silicon to software to systems

As the pace of AI and the transformation it enables across industries continues to accelerate, Microsoft is committed to building and enhancing our global cloud infrastructure to meet the needs from customers and developers with faster, more performant, and more efficient compute and AI solutions. Azure AI infrastructure comprises technology from industry leaders as well as Microsoft’s own innovations, including Azure Maia 100, Microsoft’s first in-house AI accelerator, announced in November. In this blog, we will dive deeper into the technology and journey of developing Azure Maia 100, the co-design of hardware and software from the ground up, built to run cloud-based AI workloads and optimized for Azure AI infrastructure.

Azure Maia 100, pushing the boundaries of semiconductor innovation

Maia 100 was designed to run cloud-based AI workloads, and the design of the chip was informed by Microsoft’s experience in running complex and large-scale AI workloads such as Microsoft Copilot. Maia 100 is one of the largest processors made on 5nm node using advanced packaging technology from TSMC.   

Through collaboration with Azure customers and leaders in the semiconductor ecosystem, such as foundry and EDA partners, we will continue to apply real-world workload requirements to our silicon design, optimizing the entire stack from silicon to service, and delivering the best technology to our customers to empower them to achieve more.

End-to-end systems optimization, designed for scalability and sustainability 

When developing the architecture for the Azure Maia AI accelerator series, Microsoft reimagined the end-to-end stack so that our systems could handle frontier models more efficiently and in less time. AI workloads demand infrastructure that is dramatically different from other cloud compute workloads, requiring increased power, cooling, and networking capability. Maia 100’s custom rack-level power distribution and management integrates with Azure infrastructure to achieve dynamic power optimization. Maia 100 servers are designed with a fully-custom, Ethernet-based network protocol with aggregate bandwidth of 4.8 terabits per accelerator to enable better scaling and end-to-end workload performance.  

When we developed Maia 100, we also built a dedicated “sidekick” to match the thermal profile of the chip and added rack-level, closed-loop liquid cooling to Maia 100 accelerators and their host CPUs to achieve higher efficiency. This architecture allows us to bring Maia 100 systems into our existing datacenter infrastructure, and to fit more servers into these facilities, all within our existing footprint. The Maia 100 sidekicks are also built and manufactured to meet our zero waste commitment.

Co-optimizing hardware and software from the ground up with the open-source ecosystem

From the start, transparency and collaborative advancement have been core tenets in our design philosophy as we build and develop Microsoft’s cloud infrastructure for compute and AI. Collaboration enables faster iterative development across the industry—and on the Maia 100 platform, we’ve cultivated an open community mindset from algorithmic data types to software to hardware.  

To make it easy to develop AI models on Azure AI infrastructure, Microsoft is creating the software for Maia 100 that integrates with popular open-source frameworks like PyTorch and ONNX Runtime. The software stack provides rich and comprehensive libraries, compilers, and tools to equip data scientists and developers to successfully run their models on Maia 100. 

To optimize workload performance, AI hardware typically requires development of custom kernels that are silicon-specific. We envision seamless interoperability among AI accelerators in Azure, so we have integrated Triton from OpenAI. Triton is an open-source programming language that simplifies kernel authoring by abstracting the underlying hardware. This will empower developers with complete portability and flexibility without sacrificing efficiency and the ability to target AI workloads. 

Maia 100 is also the first implementation of the Microscaling (MX) data format, an industry-standardized data format that leads to faster model training and inferencing times. Microsoft has partnered with AMD, ARM, Intel, Meta, NVIDIA, and Qualcomm to release the v1.0 MX specification through the Open Compute Project community so that the entire AI ecosystem can benefit from these algorithmic improvements.

Azure Maia 100 is a unique innovation combining state-of-the-art silicon packaging techniques, ultra-high-bandwidth networking design, modern cooling and power management, and algorithmic co-design of hardware with software. We look forward to continuing to advance our goal of making AI real by introducing more silicon, systems, and software innovations into our datacenters globally.

Source: microsoft.com

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