Introducing Azure Local: cloud infrastructure for distributed locations enabled by Azure Arc
Today at Microsoft Ignite 2024 we're introducing Azure Local, cloud-connected infrastructure that can be deployed at your physical locations and under your operational control. With Azure Local, you can run the foundational Azure compute, networking, storage, and application services locally on hardware from your preferred vendor, providing flexibility to meet your requirements and budget.Announcing General Availability: Windows Server Management enabled by Azure Arc
Windows Server Management enabled by Azure Arc offers customers with Windows Server licenses that have active Software Assurances or Windows Server licenses that are active subscription licenses the following key benefits: Azure Update Manager Azure Change Tracking and Inventory Azure Machine Configuration Windows Admin Center in Azure for Arc Remote Support Network HUD Best Practices Assessment Azure Site Recovery (Configuration Only) Upon attestation, customers receive access to the following at no additional cost beyond associated networking, compute, storage, and log ingestion charges. These same capabilities are also available for customers enrolled in Windows Server 2025 Pay as you Go licensing enabled by Azure Arc. Learn more at Windows Server Management enabled by Azure Arc - Azure Arc | Microsoft Learn or watch Video: Free Azure Services for Non-Azure Windows Servers Covered by SA Powered by Azure Arc! To get started, connect your servers to Azure Arc, attest for these benefits, and deploy management services as you modernize to Azure's AI-enabled set of server management capabilities across your hybrid, multi-cloud, and edge infrastructure!Introducing Azure AI Agent Service
Introducing Azure AI Agent Service at Microsoft Ignite 2024 Discover how Azure AI Agent Service is revolutionizing the development and deployment of AI agents. This service empowers developers to build, deploy, and scale high-quality AI agents tailored to business needs within hours. With features like rapid development, extensive data connections, flexible model selection, and enterprise-grade security, Azure AI Agent Service sets a new standard in AI automationExtending Azure's AI Platform with an adaptive cloud approach
Authored by Derek Bogardus and Sanjana Mohan, Azure Edge AI Product Management Ignite 2024 is here, and nothing is more top of mind for customers than the potential to transform their businesses with AI wherever they operate. Today, we are excited to announce the preview of two new Arc-enabled services that extend the power of Azure’s AI platform to on-premises and edge environments. Sign up to join the previews here! An adaptive cloud approach to AI The goal of Azure’s adaptive cloud approach is to extend just enough Azure to customers’ distributed environments. For many of these customers, valuable data is generated and stored locally, outside of the hyperscale cloud, whether due to regulation, latency, business continuity, or simply the large volume of data being generated in real time. AI inferencing can only occur where the data exists. So, while the cloud has become the environment of choice for training models, we see a tremendous need to extend inferencing services beyond the cloud to enable complete cloud-to-edge AI scenarios. Search on-premises data with generative AI Over the past couple of years, generative AI has come to the forefront of AI innovation. Language models give any user the ability to interact with large, complex data sets in natural language. Public tools like ChatGPT are great for queries about general knowledge, but they can’t answer questions about private enterprise data on which they were not trained. Retrieval Augmented Generation, or "RAG", helps address this need by augmenting language models with private data. Cloud services like Azure AI Search and Azure AI Foundry simplify how customers can use RAG to ground language models in their enterprise data. Today, we are announcing the preview of a new service that brings generative AI and RAG to your data at the edge. Within minutes, customers can deploy an Arc extension that contains everything needed to start asking questions about their on-premises data, including: Popular small and large language models running locally with support for both CPU and GPU hardware A turnkey data ingestion and RAG pipeline that keeps all data completely local, with RBAC controls to prevent unauthorized access An out-of-the-box prompt engineering and evaluation tool to find the best settings for a particular dataset Azure-consistent APIs to integrate into business applications, as well as a pre-packaged UI to get started quickly This service is available now in gated private preview for customers running Azure Local infrastructure, and we plan to make it available on other Arc-enabled infrastructure platforms in the near future. Sign up here! Deploy curated open-source AI models via Azure Arc Another great thing about Azure’s AI platform is that it provides a catalog of curated AI models that are ready to deploy and provide consistent inferencing endpoints that can be integrated directly into customer applications. This not only makes deployment easy, but customers can also be confident that the models are secure and validated These same needs exist on the edge as well, which is why we are now making a set of curated models deployable directly from the Azure Portal. These models have been selected, packaged, and tested specifically for edge deployments, and are currently available on Azure Local infrastructure. Phi-3.5 Mini (3.8 billion parameter language model) Mistral 7B (7.3 billion parameter language model) MMDetection YOLO (object detection) OpenAI Whisper Large (speech to text) Google T5 Base (translation) Models can be deployed from a familiar Azure Portal wizard to an Arc AKS cluster running on premises. All available models today can be run on just a CPU. Phi-3.5 and Mistral 7B also have GPU versions available for better performance. Once complete, the deployment can be managed directly in Azure ML Studio, and an inferencing endpoint is available on your local network. Wrap up Sign up now to join either of the previews at the link below or stop by and visit us in person in the Azure Arc and Azure Local Expert Meet Up station in the Azure Infrastructure neighborhood at Ignite. We’re excited to get these new capabilities into our customers’ hands and hear from you how it’s going. Sign up to join the previews hereAnnouncing the Next generation Azure Data Box Devices
Microsoft Azure Data Box offline data transfer solution allows you to send petabytes of data into Azure Storage in a quick, inexpensive, and reliable manner. The secure data transfer is accelerated by hardware transfer devices that enable offline data ingestion to Azure. Our customers use the Data Box family to move petabytes-scale data into Azure for backup, archival, data analytics, media and entertainment, training, and different workload migrations etc. We continue to get requests about moving truly massive amounts of data in a secure, simple and quick manner. We’ve heard you and to address your needs, we’ve designed a new, enhanced product to meet your data transfer needs. About the latest innovation in Azure Data Box Family Today, we’re excited to announce the preview of Azure Data Box 120 and Azure Data Box 525, our next-generation compact, NVMe-based Data Box devices. The new offerings reflect insights gained from working with our customers over the years and understanding their evolving data transfer needs. These new devices incorporate several improvements to accelerate offline data transfers to Azure, including: Fast copy - Built with NVMe drives for high-speed transfers and improved reliability and support for faster network connections Easy to use - larger capacity offering (525 TB) in a compact form-factor for easy handling Resilient - Ruggedized devices built to withstand rough conditions during transport Secure - Enhanced physical, hardware and software security features Broader availability – Presence in more Azure regions, meeting local compliance standards and regulations What’s new? Improved Speed & Efficiency NVMe devices offer faster data transfer rates, with copy speeds up to 7 GBps via SMB Direct on RDMA (100-GbE) for medium to large files, a 10x improvement in data transfer speeds as compared to previous generation devices. High-speed transfers to Azure with data upload up to 5x faster for medium to large files, reducing the lead time for your data to become accessible in the Azure cloud. Improved networking with support for up to 100 GbE connections, as compared to 10 GbE on the older generation of devices. Two options with usable capacity of 120 TB and 525 TB in a compact form factor meeting OSHA requirements. Devices ship the next day air in most regions. Learn more about the performance improvements on Data Box 120 and Data Box 525. Enhanced Security The next generation devices come with several new physical, hardware and software security enhancements. This is in addition to the built in Azure security baseline for Data Box and Data Box service security measures currently supported by the service. Secure boot functionality with hardware root of trust and Trusted Platform Module (TPM) 2.0. Custom tamper-proof screws and built-in intrusion detection system to detect unauthorized device access. AES 256-bit BitLocker software encryption for data at rest is currently available. Hardware encryption via the RAID controller, which will be enabled by default on these devices, is coming soon. Furthermore, once available, customers can enable double encryption through both software and hardware encryption to meet their sensitive data transfer requirements. These ISTA 6A compliant devices are built to withstand rough conditions during shipment while keeping both the device and your data safe and intact. Learn more about the enhanced security features on Data Box 120 and Data Box 525. Broader Azure region coverage Recurring request from our customers has been for wider availability of our higher-capacity device to ease large migrations. We’re happy to share Data Box 525 will be available across most Azure regions where the Data Box service is currently live. This marks a significant improvement in availability of a large-capacity device as compared to the current Data Box Heavy. What our customers have to say For the last several months, we’ve been working directly with our customers of all industries and sizes to leverage the next generation devices for their data migration needs. Customers love the larger capacity with form-factor familiarity, seamless set up and faster copy. “This new offering brings significant advantages, particularly by simplifying our internal processes. With deployments ranging from hundreds of terabytes to even petabytes, we previously relied on multiple regular Data Box devices—or occasionally Data Box Heavy devices—which required extensive operational effort. The new solution offers sizes better aligned with our needs, allowing us to achieve optimal results with fewer logistical steps. Additionally, the latest generation is faster and provides more connectivity options at data centre premises, enhancing both efficiency and flexibility for large-scale data transfers.” - Lukasz Konarzewski, Senior Data Architect, Commvault “We have been using the devices to move 1PB of archival media data to Azure blob storage using the Data Box transfer devices. The next generation devices provided a very smooth setup and copy experience, and we were able to transfer data in larger chunks and much faster than before. Overall, this has helped shorten our migration lead times and land the data in the cloud quickly and seamlessly.” - Daniel Perry, Kohler “We have had a positive experience overall with the new Data Box devices to move our data to Azure Blob storage. The devices offer easy plug and play installation, detailed documentation especially for the security features and good data copy performance. We would definitely consider using it again for future large data migration projects.” – Bas Boeijink, Cloud Engineer, Eurofiber Cloud Infra Sign up for the Preview The Preview is available in the US, Canada, EU, UK, and US Gov Azure regions, and we will continue to expand to more regions in the coming months. If you are interested in the preview, we want to hear from you. Customers can sign up here ISV partners can sign up here You can learn more about the all-new Data Box devices here. We are committed to continuing to deliver innovative solutions to lower the barrier for bringing data to Azure. Your feedback is important to us. Tell us what you think about the new Azure Data Box preview by writing to us at DataBoxPM@microsoft.com – we can’t wait to hear from you. Stop by and see us! Now that you’ve heard about the latest innovation in the product family, do come by and see the new devices at the Ignite session What’s new in Azure Storage: Supercharge your data centric workloads, on 21st November starting 11:00 AM CST. You can also drop by the Infra Hub to learn more from our product experts and sign up to try the new devices for your next migration!Ignite 2024: AKS enabled by Azure Arc - New Capabilities and Expanded Workload Support
Microsoft Ignite 2024 has been a showcase of innovation across the Azure ecosystem, bringing forward major advancements in AI, cloud-native applications, and hybrid cloud solutions. This year’s event featured key updates, including enhancements to AKS enabled by Azure Arc, which introduced new capabilities and expanded workload support. These updates reinforce the value and versatility that AKS enabled by Azure Arc brings to organizations looking to scale and optimize their operations. With these advancements, AKS Arc continues to support seamless management, increased scalability, and enhanced workload performance across diverse infrastructures. AKS Enabled by Azure Arc AKS enabled by Azure Arc brings the power of Azure’s managed Kubernetes service to any environment, providing consistent management and security across on-premises, edge, and multi-cloud deployments. It encompasses: AKS on Azure Local: A full-featured Kubernetes platform integrated with Azure Local for comprehensive container orchestration in hybrid setups. Notably, AKS on Azure Local has earned recognition as a leader in the 2024 Gartner Magic Quadrant for Distributed Hybrid Infrastructure, underscoring Microsoft's dedication to delivering comprehensive, enterprise-ready solutions for hybrid cloud deployments. AKS Edge Essentials: A lightweight version designed for edge computing, ensuring operational consistency on constrained hardware. AKS on Azure Local Disconnected Operations: It is now available on Azure Local Disconnected Operations. This latest addition to AKS enabled by Azure Arc portfolio is the support for fully disconnected scenario. It allows AKS enabled by Azure Arc to operate in air-gapped, isolated environments without the need for continuous Azure connectivity. It is crucial for organizations that require secure, self-sufficient Kubernetes operations in highly controlled or remote locations. With this support, businesses can maintain robust Kubernetes functionality while meeting stringent compliance and security standards. Key Features and Expanded Workload Support This year's Ignite announcements unveiled a series of public preview and GA features that enhance the capabilities of AKS enabled by Azure Arc. These advancements reflect our commitment to delivering robust, scalable solutions that meet the evolving needs of our customers. Below are the key highlights that showcase the enhanced capabilities of AKS enabled by Azure Arc: Edge Workload Azure IoT Operations - enabled by Azure Arc: Available on AKS Edge Essentials (AKS-EE) and AKS on Azure Local with public preview support. Azure IoT Operations in the management and scaling of IoT solutions. It provides robust support for deploying and overseeing IoT applications within Kubernetes environments, enhancing operational control and scalability. Organizations can leverage this tool to maintain seamless management of distributed IoT workloads, ensuring consistent performance and simplified scaling across diverse deployment scenarios. Azure Container Storage - enabled by Azure Arc: Available on both AKS Edge Essentials (AKS-EE) and AKS on Azure Local, this support enables seamless integration for persistent storage needs in Kubernetes environments. It provides scalable, reliable, and high-performance storage solutions that enhance data management and support stateful applications running in hybrid and edge deployments. This addition ensures that organizations can efficiently manage their containerized workloads with robust storage capabilities. Azure Key Vault Secret Store extension for Kubernetes: Now available as public preview on AKS Edge Essentials and AKS on Azure Local, this extension automatically synchronizes secrets from an Azure Key Vault to an AKS enabled by Azure Arc cluster for offline access, providing essential tools for proactive monitoring and policy enforcement. It offers advanced security and compliance capabilities tailored for robust governance and regulatory adherence, ensuring that organizations can maintain compliance with industry standards and best practices while safeguarding their infrastructure. Azure Monitor Pipeline: The Azure Monitor pipeline is a data ingestion solution designed to provide consistent, centralized data collection for Azure Monitor. Once deployed for AIO on AKS cluster enabled by Azure Arc, it enables at-scale telemetry data collection and routing at the edge. The pipeline can cache data locally, syncing with the cloud when connectivity is restored, and supports segmented networks where direct data transfer to the cloud isn’t possible. Built on OpenTelemetry Collector, the pipeline’s configuration includes data flows, cache properties, and destination rules defined in the DCR to ensure seamless data processing and transmission to the cloud. Arc Workload Identity Federation: Now available as public preview on AKS Edge Essentials and AKS on Azure Local, providing secure federated identity management to enhance security for customer workloads Arc Gateway: Now available as public preview for AKS Edge Essentials and AKS on Azure Local. Arc Gateway support on AKS enabled by Azure Arc enhances secure connectivity across hybrid environments, reducing required firewall rules and improving security for customer deployments. Azure AI Video Indexer - enabled by Azure Arc: Supported on AKS Edge Essentials and AKS on Azure Local. Arc-enabled Video Indexer enables comprehensive AI-powered video analysis, including transcription, facial recognition, and object detection. It allows organizations to deploy sophisticated video processing solutions within hybrid and edge environments, ensuring efficient local data processing with improved security and minimal latency. MetalLB - Azure Arc Extension: Now supported on AKS Edge Essentials and AKS on Azure Local, MetalLB ensures efficient load balancing capabilities. This addition enhances network resilience and optimizes traffic distribution within Kubernetes environments. Comprehensive AI and Machine Learning Capabilities GPUs for AI Workloads: Now AKS enabled by Azure Arc supports a range of GPUs tailored for AI and machine learning workloads with GPU Partitioning) and GPU Passthrough Virtualization support. These options enable robust performance for resource-intensive AI and machine learning workloads, allowing for efficient use of GPU resources to run complex models and data processing tasks. Arc-enabled Azure Machine Learning: Support on AKS on Azure Local, AML capabilities for running sophisticated AI models. Businesses can leverage Azure’s powerful machine learning tools seamlessly across different environments, enabling them to develop, deploy, and manage machine learning models effectively on-premises and at the edge. Arc-enabled Video Indexer: It extends Azure's advanced video analytics capabilities to AKS enabled by Azure Arc. Organizations can now process and analyze video content in real-time, harnessing Azure's robust video AI tools to enhance video-based insights and operations. This support provides businesses with greater flexibility to conduct video analysis seamlessly in remote or hybrid environments Kubernetes AI Toolchain Orchestrator (Kaito + LoRA + QLoRA): Fully validated and support for fine-tuning and optimizing AI models, Kaito, LoRA and QLoRA are designed for edge deployments such as AKS on Azure Local. This combination enhances the ability to run and refine AI applications effectively in edge environments, ensuring performance and flexibility. Flyte Integration: Now supported on AKS on Azure Local, Flyte offers a scalable orchestration platform for managing machine learning workflows. This capability enables teams to build, execute, and manage complex AI pipelines efficiently, enhancing productivity and simplifying the workflow management process. Enhanced Infrastructure and Operations Management Infrastructure as Code (IaC) with Terraform: Now supported on AKS on Azure Local for both Connected and Air-gapped scenario, providing streamlined deployment capabilities through code. This support enables teams to automate and manage their Kubernetes infrastructure at scale more efficiently with Terraform. Anti-affinity, Pod CIDR, Taints/Labels: Available on AKS on Azure Local, these features provide enhanced infrastructure capabilities by allowing refined workload placement and advanced network configuration. Anti-affinity rules help distribute pods across different nodes to avoid single points of failure, while Pod CIDR simplifies network management by allocating IP ranges to pods. Taints and labels offer greater control over node selection, ensuring that specific workloads run on designated nodes and enhancing the overall efficiency and reliability of Kubernetes operations. Optimized Windows Node Pool Management: AKS enabled by Azure Arc now includes the capability to enable and disable Windows node pools for clusters. This enhancement helps prevent unnecessary binary downloads, benefiting customers with low-speed or limited internet connection. It optimizes resource usage, reduces bandwidth consumption, and enhances overall deployment efficiency, making it ideal for environments with network constraints. Kubernetes Development AKS-WSL: With AKS-WSL, developers can set up a local environment that mimics the experience of working with AKS. This makes it easier for developers to write, debug, and test Kubernetes applications locally before deploying them to a full AKS cluster. AKS-WSL VSCode Extension: The Visual Studio Code extension for AKS-WSL allows developers to write, debug, and deploy Kubernetes applications locally, streamlining development workflows. This setup improves productivity by providing efficient tools and capabilities, making it easier to develop, test, and refine Kubernetes workloads directly from a local machine. Arc Jumpstart: Supported AKS Edge Essentials and AKS on Azure Local. Arc Jumpstart simplifies deployment initiation, providing developers with a streamlined way to set up and start working with Kubernetes environments quickly. It makes it easier for teams to evaluate and experiment with AKS enabled by Azure Arc, offering pre-configured scenarios and comprehensive guidance. By reducing complexity and setup time, Arc Jumpstart enhances the developer experience, facilitating faster prototyping and smoother onboarding for new projects in hybrid and edge settings. Conclusion Microsoft Ignite 2024 has underscored the continued evolution of AKS enabled by Azure Arc, bringing more comprehensive, scalable, and secure solutions to diverse environments. These advancements support organizations in running cloud-native applications anywhere, enhancing operational efficiency and innovation. We welcome your feedback (aksarcfeedback@microsoft.com) and look forward to ongoing collaboration as we continue to evolve AKS enabled by Azure Arc.New Da/Ea/Fav6 VMs with increased performance and Azure Boost are now generally available
By Sasha Melamed, Senior Product Manager, Azure Compute We are excited to announce General Availability of new Dalsv6, Dasv6, Easv6, Falsv6, Fasv6, and Famsv6-series Azure Virtual Machines (VMs) based on the 4th Gen AMD EPYC™ processor (Genoa). These VMs deliver significantly improved performance and price/performance versus the prior Dasv5 and Easv5 VMs, NVMe connectivity for faster local and remote storage access, and Azure Boost for improved performance and enhanced security. With the broad selection of compute, memory, and storage configurations available with these new VM series, there is a best fit option for a wide range of workloads. What’s New The new Dalsv6, Davs6, and Easv6 VMs are offered with vCPU counts ranging from 2 to 96 vCPUs. The new general purpose and memory optimized VMs will come in a variety of memory (GiB)-to-vCPU ratios, including the Dalsv6 at 2:1, Dasv6 at 4:1, and Easv6 at 8:1 ratios. The VMs are also available with and without a local disk so that you can choose the option that best fits your workload. Workloads can expect up to 20% CPU performance improvement over the Dasv5 and Easv5 VMs and up to 15% better price/performance. Further expanding our offerings, we are proud to introduce the first Compute-optimized VM series based on AMD processors also in three memory-to-vCPU ratios. The new Falsv6, Fasv6, and Famsv6 VMs offer the fastest x86 CPU performance in Azure and have up to 2x CPU performance improvement over our previous v5 VMs, as shown in the graph below. We are excited to announce that the new Dalsv6, Dasv6, Easv6, and suite of Fasv6 virtual machines are powered by Azure Boost. Azure Boost has been providing benefits to millions of existing Azure VMs in production today, such as enabling exceptional remote storage performance and significant improvements in networking throughput and latency. Our latest Azure Boost infrastructure innovation, in combination with new AMD-based VMs, delivers improvements in performance, security, and reliability. The platform provides sub-second servicing capabilities for the most common infrastructure updates, delivering a 10x reduction in impact. To learn more about Azure Boost, read our blog. To drive the best storage performance for your workloads, the new AMD-based VMs come with the NVMe interface for local and remote disks. Many workloads will benefit from improvements over the previous generation of AMD-based with up to: 80% better remote storage performance 400% faster local storage speeds 25% networking bandwidth improvement 45% higher NVMe SSD capacity per vCPU for Daldsv6, Dadsv6, Eadsv6-series VMs with local disks The 4th Gen AMD EPYC™ processors provide new capabilities for these VMs, including: Always-On Transparent Secure Memory Encryption ensuring that your sensitive information remains secure without compromising performance. AVX-512 to handle compute-intensive tasks such as scientific simulations, financial analytics, AI, and machine learning. Vector Neural Network Instructions enhancing the performance of neural network inference operations, making it easier to deploy and scale AI solutions. Bfloat16 for efficient training and inference of deep learning models, providing a balance between performance and precision. Dasv6, Dadsv6, Easv6, Eadsv6, Fasv6, and Fadsv6-series VMs are SAP Certified. Whether you’re running a simple test infrastructure, mission critical enterprise applications, high-performance computing tasks, or AI workloads, our new VMs are ready to meet your needs. Explore the new capabilities and start leveraging the power of Azure today! General-purpose workloads The new Dasv6-series VMs offer a balanced ratio of memory to vCPU performance and increased scalability, up to 96 vCPUs and 384 GiB of RAM. Whereas the new Dalsv6-series VM series are ideal for workloads that require less RAM per vCPU, with a max of 192 GiB of RAM. The Dalsv6 series are the first 2GiB/vCPU memory offerings in our family of AMD-based VMs. The Dalsv6 series can reduce your costs when running non-memory intensive applications, including web servers, gaming, video encoding, AI/ML, and batch processing. The Dasv6-series VMs work well for many general computing workloads, such as e-commerce systems, web front ends, desktop virtualization solutions, customer relationship management applications, entry-level and mid-range databases, application servers, and more. Series vCPU Memory (GiB) Max Local NVMe Disk (GiB) Max IOPS for Local Disk Max Uncached Disk IOPS for Managed Disks Max Managed Disks Throughput (MBps) Dalsv6 2-96 4-192 N/A N/A 4 - 172K 90 – 4,320 Daldsv6 2-96 4-192 1x110 - 6x880 1.8M 4 - 172K 90 – 4,320 Dasv6 2-96 8-384 N/A N/A 4 - 172K 90 – 4,320 Dadsv6 2-96 8-384 1x110 - 6x880 1.8M 4 - 172K 90 – 4,320 Memory-intensive workloads For more memory demanding workloads, the new Easv6-series VMs offer high memory-to-vCPU ratios with increased scalability up to 96 vCPUs and 672 GiB of RAM. The Easv6-series VMs are ideal for memory-intensive enterprise applications, data warehousing, business intelligence, in-memory analytics, and financial transactions. Series vCPU Memory (GiB) Max Local NVMe Disk (GiB) Max IOPS for Local Disk Max Uncached Disk IOPS for Managed Disks Max Managed Disks Throughput (MBps) Easv6 2-96 16-672 N/A N/A 4 - 172K 90 – 4,320 Eadsv6 2-96 16-672 1x110 - 6x880 1.8M 4 - 172K 90 – 4,320 Compute-intensive workloads For compute-intensive workloads, the new Falsv6, Fasv6 and Famsv6 VM series come without Simultaneous Multithreading (SMT), meaning a vCPU equals one physical core. These VMs will be the best fit for workloads demanding the highest CPU performance, such as scientific simulations, financial modeling and risk analysis, gaming, and video rendering. Series vCPU Memory (GiB) Max Uncached Disk IOPS for Managed Disks Max Managed Disks Throughput (MBps) Max Network Bandwidth (Gbps) Falsv6 2-64 4-128 4 - 115K 90 - 2,880 12.5 - 36 Fasv6 2-64 8-256 4 - 115K 90 - 2,880 12.5 - 36 Famsv6 2-64 16-512 4 - 115K 90 - 2,880 12.5 - 36 Customers are excited about new AMD v6 VMs FlashGrid offers software solutions that help Oracle Database users on Azure achieve maximum database uptime and minimize the risk of outages. The Easv6 series VMs make it easier to support Oracle RAC workloads with heavy transaction processing on Azure using FlashGrid Cluster. The NVMe protocol enhances disk error handling, which is important for failure isolation in high-availability database architectures. The CPU boost frequency of 3.7 GHz and higher network bandwidth per vCPU enable database clusters to handle spikes in client transactions better while keeping a lower count of vCPU to limit licensing costs. The Easv6 VMs have passed our extensive reliability and compatibility testing and are now available for new deployments and upgrades. – Art Danielov, CEO, FlashGrid Inc. Helio is a platform for large-scale computing workloads, optimizing for costs, scale, and emissions. Its main focus is 3D rendering Our architectural and media & entertainment (VFX) 3D rendering workloads have been accelerated by an average of ~42% with the new v6 generation, while maintaining low cost and high scale. In addition, we are seeing significant improvements in disk performance with the new NVMe interface, resulting in much faster render asset load times. -- Kevin Häfeli, CEO / Cofounder Helio AG Silk's Software-Defined Cloud Storage delivers unparalleled price/performance for the most demanding, real-time applications. Silk has tested the new Da/Eav6 VM offering from Azure and we are looking forward to enable our customers to benefit from its new capabilities, allowing higher throughput at lower cost, while providing increased reliability” -- Adik Sokolovski, Chief R&D Officer, Silk ZeniMax Online Studios creates online RPG worlds where you can play and create your own stories. The new VMs we tested provided a significant performance boost in our build tasks. The super-fast storage not only made the workflows smoother and faster, but it also helped highlight other bottlenecks in our design and allowed us to improve our pipeline overall. We are excited for their availability and plan on utilizing these machines to expand our workload in Azure. -- Merrick Moss, Product Owner, ZeniMax Online Studios Getting started The new VMs are now available in the East US, East US 2, Central US, South Central US, West US 3, West Europe, and North Europe regions with more to follow. Check out pricing on the following pages for Windows and Linux. You can learn more about the new VMs in the documentation for Dal-series, Da-series, Ea-series, and Fa-series. We also recommend reading the NVMe overview and FAQ. You can find the Ultra disk and Premium SSD V2 regional availability to pair with the new NVMe based v6 series at their respective links.AI Toolkit for Visual Studio Code: October 2024 Update Highlights
The AI Toolkit’s October 2024 update revolutionizes Visual Studio Code with game-changing features for developers, researchers, and enthusiasts. Explore multi-model integration, including GitHub Models, ONNX, and Google Gemini, alongside custom model support. Dive into multi-modal capabilities for richer AI testing and seamless multi-platform compatibility across Windows, macOS, and Linux. Tailored for productivity, the enhanced Model Catalog simplifies choosing the best tools for your projects. Try it now and share feedback to shape the future of AI in VS Code!
Announcing an accelerator for GenAI-powered assistants using Azure AI Language and Azure OpenAI
We’re thrilled to introduce a new accelerator solution in GitHub Azure-Samples library designed specifically for creating and enhancing your GenAI-based conversational assistants with robust, human-controllable workflows. This accelerator uses key services from Azure AI Language in addition to Azure OpenAI, including PII detection to protect sensitive information, Conversational Language Understanding (CLU) to predict top users’ intents, Custom Question Answering (CQA) to respond to top questions with deterministic answers. Together with Azure OpenAI and Large Language Models (LLMs), the solution is designed to orchestrate and deliver a smooth, human-guided, controllable and deterministic conversational experience. The integration with LLMs will come soon. It’s perfect for developers and organizations looking to build assistants that can handle complex queries, route tasks, and provide reliable answers, all with a controlled, scalable architecture. Why This Accelerator While LLMs have been appreciated by many customers to build conversational assistants for natural, engaging, and context-aware interactions, there are challenges such as the significant efforts required in prompt engineering, document chunking, and reducing hallucinations to improve the quality of their Retrieval-Augmented Generation (RAG) solutions. If an AI quality issue is discovered in production, customers need to find an effective way to address it promptly. This solution aims to help customers utilize offerings in the Azure AI portfolio and address key challenges when building Generative AI (GenAI) assistants. Designed for flexibility and reliability, this accelerator enables human-controllable workflows that meet real-world customer needs. It minimizes the need for extensive prompt engineering by using a structured workflow to prioritize top questions with exact answers and custom intents that are critical to your business and use LLM to handle topics in a conversation that have lower priorities. This architecture not only enhances answer quality and control but also ensures that complex queries are handled efficiently. If you want to fix quickly an incorrect answer for your chatbot built with RAG, you can also attach this accelerator solution to your existing RAG solution and quickly add a QA pair with the correct response in CQA to fix the issue for your users. What This Accelerator Delivers This accelerator provides and demonstrates an end-to-end orchestration using a few capabilities in Azure AI Language and Azure OpenAI for conversational assistants. It can be applied in various scenarios where control over assistant behavior and response quality is essential, like call centers, help desks, and other customer support applications. Below is a reference architecture of the solutions: Key components of this solution include (components in dash boxes coming soon): Client-Side User Interface for Demonstration (coming soon) A web-based client-side interface is included in the accelerator solution, to showcase the accelerator solution in an interactive, user-friendly format. This web UI allows you to quickly explore and test this solution, such as its orchestration routing behavior and functionalities. Workflow Orchestration for Human-Controllable Conversations By combining services like CLU, CQA, and LLMs, the accelerator allows for a dynamic, adaptable workflow. CLU can recognize and route customer-defined intents, while CQA provides exact answers from predefined QA pairs. If a question falls outside the pre-defined scope, the workflow can seamlessly fall back to LLMs, which is enhanced with RAG for contextually relevant, accurate responses. This workflow ensures human-like adaptability while maintaining control over assistant responses. Conversational Language Understanding (CLU) for Intent Routing The CLU service allows you to define the top intents you want the assistants to handle. The top intents can be those critical to your business and/or those most users ask your assistants. This component plays a central role in directing conversations by interpreting user intents and routing them to the right action or AI agents. Whether completing a task or addressing specific customer needs, CLU provides the mechanism to ensure the assistant accurately understands and executes the process of handling custom-defined intents. Custom Question Answering (CQA) for Exact Answers and with No Hallucinations CQA allows you to create and manage predefined QA pairs to deliver precise responses, reducing ambiguity and ensuring that the assistant aligns closely with defined answers. This controlled response approach maintains consistency in interactions, improving reliability, particularly for high-stake or regulatory-sensitive conversations. You can also attach CQA to your existing RAG solution to quickly fix incorrect answers. PII Detection and Redaction for Privacy Protection (coming soon) Protecting user privacy is a top priority, especially in conversational AI. This accelerator showcases an optional integration of Azure AI Language’s Personally Identifiable Information (PII) to automatically identify and redact sensitive information, if compliance with privacy standards and regulations is required LLM with RAG to Handle Everything Else (coming soon) In this accelerator, we are using a RAG solution to handle missed intents or user queries on lower-priority topics. This RAG solution can be replaced with your existing one. The predefined intents and question-answer pairs can be appended and updated over time based on evolving business needs and DSATs (dissatisfaction) discovered in the RAG responses. This approach ensures controlled and deterministic experiences for high-value or high-priority topics while maintaining flexibility and extensibility for lower-priority interactions. Components Configuration for "Plug-and-Play" One of the standout features of this accelerator is its flexibility through a "plug-and-play" component configuration. The architecture is designed to allow you to easily swap, add, or remove components to tailor the solution to your specific needs. Whether you want to add custom intents, adjust fallback mechanisms, or incorporate additional data sources, the modular nature of the accelerator makes it simple to configure. Get Started Building Your GenAI-Powered Assistant Today Our new accelerator is available on GitHub, ready for developers to deploy, customize, and use as a foundation for your own needs. Join us as we move towards a future where GenAI can empower organizations to meet business needs with intelligent, adaptable, and human-controllable assistants. What’s more: Other New Azure AI Language Releases This Ignite Beyond these, Azure AI Language provides additional capabilities to support GenAI customers in more scenarios to ensure quality, privacy and flexible deployment in any types of environments, either clouds or on premises. We are also excited to announce the following new features launching at Ignite. Azure AI Language in Azure AI Studio: Azure AI Language is moving to AI Studio. Extract PII from text, Extract PII from conversation, Summarize text, Summarize conversation, Summarize for call center, and Text Analytics for health are now available in AI Studio playground. More skills follow. Conversational Language Understanding (CLU): Today, customers use CLU to build custom natural language understanding models hosted by Azure to predict the overall intention of an incoming utterance and extract important information from it. However, some customers have specific needs that require an on-premise connection. We are excited to announce runtime containers for CLU for these specific use cases. PII Detection and Redaction: Azure AI Language offers Text PII and Conversational PII services to extract personally identifiable information from input text and conversation to enhance privacy and security, oftentimes before sending data to the cloud or an LLM. We are excited to announce new improvements to these services - the preview API (version 2024-11-15-preview) now supports the option to mask detected sensitive entities with a label (i.e. “John Doe received a call from 424-878-9192” can now be masked with an entity label, i.e. . “[PERSON_1] received a call from [PHONENUMBER_1]”. More on how to specify the redaction policy style for your outputs can be found in our documentation. Native document support: The gating control is removed with the latest API version, 2024-11-15-preview, allowing customers to access native document support for PII redaction and summarization. Key updates in this version include: - Increased Maximum File Size Limits (from 1 MB to 10 MB). - Enhanced PII Redaction Customization: Customers can now specify whether they want only the redacted document or both the redacted document and a JSON file containing the detected entities. Language detection: Language detection is a preconfigured feature that can detect the language a document is written in and returns a language code for a wide range of languages, variants, dialects, and some regional/cultural languages. We are happy to announce today the general availability of scription detection capability, and 16 more languages support, which adds up to 139 total supported languages. Named entity recognition (NER): The Named Entity Recognition (NER) service supports customer scenarios for identifying and analyzing entities such as addresses, names, and phone numbers from inputs text. NER’s Generally Available API (version 2024-11-01) now supports several optional input parameters (inclusionList, exclusionList, inferenceOptions, and overlapPolicy) as well as an updated output structure (with new fields tags, type, and metadata) to ensure enhanced user customization and deeper analysis. More on how to use these parameters can be found in our documentation. Text analytics for health: Text analytics for health (TA4H) is a preconfigured feature that extracts and labels relevant medical information from unstructured texts such as doctor's notes, discharge summaries, clinical documents, and electronic health records. Today, we released support for Fast Healthcare Interoperability Resources (FHIR) structuring and temporal assertion detection in the Generally Available API.
