Microsoft Industrial AI Partner Guide: Choosing the Right Data Expertise for Every Stage
As organizations scale Industrial AI, the challenge shifts from technology selection to deciding who should lead which part of the journey -- and when. Which partners should establish secure connectivity? Who enables production grade, AI ready industrial data? When do systems integrators step in to scale globally? This Partner Guide helps customers navigate these decisions with clarity and confidence: Identify which partners align to their current digital transformation and Industrial AI scenarios leveraging Azure IoT and Azure IoT Operations Confidently combine partners over time as they evolve from connectivity to intelligence to autonomous operations This guide focuses on the Industrial AI data plane – the partners and capabilities that extract, contextualize, and operationalize industrial data so it can reliably power AI at scale. It does not attempt to catalog or prescribe end‑to‑end Industrial AI applications or cloud‑hosted AI solutions. Instead, it helps customers understand how industrial partners create the trusted, contextualized data foundation upon which AI solutions can be built. Common Customer Journey Steps 1. Modernize Connectivity & Edge Foundations The industrial transformation journey starts with securely accessing operational data without touching deterministic control loops. Customers connect automation systems to a scalable, standards-based data foundation that modernizes operations while preserving safety, uptime and control. Outcomes customers realize Standardized OT data access across plants and sites Faster onboarding of legacy and new assets Clear OT–IT boundaries that protect safety and uptime Partner strengths at this stage Industrial hardware and edge infrastructure providers Protocol translation and OT connectivity Automation and edge platforms aligned with Azure IoT Operations 2. Accelerate Insights with Industrial AI With a consistent edge-to-cloud data plane in place, customers move beyond dashboards to repeatable, production-grade Industrial AI use cases. Customers rely on expert partners to turn standardized operational data into AI‑ready signals that can be consumed by analytics and AI solutions at scale across assets, lines, and sites. Outcomes customers realize Improved Operational efficiency and performance Adaptive facilities and production quality intelligence Energy, safety, and defect detection at scale Partner strengths at this stage Industrial data services that contextualize and standardize OT signals for AI consumption Domain-specific acceleration for common Industrial AI scenarios Data pipelines integrated with Azure IoT Operations and Microsoft Fabric 3. Prepare for Autonomous Operations As organizations advance toward closed‑loop optimization, the focus shifts to safe, scalable autonomy. Customers depend on partners to align data, infrastructure, and operational interfaces, while ensuring ongoing monitoring, governance, and lifecycle management across the full operational estate. Outcomes customers realize Proven reference architectures deployed across plants AI‑ready data foundations that adapt as operations scale Coordinated interaction between OT systems, AI models, and cloud intelligence Partner strengths at this stage Industrial automation leadership and control system expertise Edge infrastructure optimized and ready for Industrial AI scale Systems integrators enabling end‑to‑end implementation and repeatability Data Intelligence Plane of Industrial AI - Partner Matrix This matrix highlights which partners have the deepest expertise in accessing, contextualizing, and operationalizing industrial data so it can reliably power AI at scale. The matrix is not a catalog of end‑to‑end Industrial AI applications; it shows how specialized partners contribute data, infrastructure, and integration capabilities on a shared Azure foundation as organizations progress from connectivity to insight to autonomous operations. How to use this matrix: Start with your scenario → identify primary partner types → layer complementary partners as you scale. Partner Type Adaptive Cloud Primary Solution Example Scenarios Geography Advantech Industrial Hardware, Industrial Connectivity LoRaWAN gateway integration + Azure IoT Operations Industrial edge platforms with built in connectivity, industrial compute, LoRaWAN, sensor networks Global Accenture GSI Industrial AI, Digital Transformation, Modernization OEE, predictive maintenance, real-time defect detection, optimize supply chains, intelligent automation and robotics, energy efficiency Global Avanade GSI Factory Agents and Analytics based on Manufacturing Data Solutions Yield / Quality optimization, OEE, Agentic Root Cause Analysis and process optimization; Unified ISA-95 Manufacturing Data estate on MS Fabric Global Capgemini GSI The new AI imperative in manufacturing OEE, maintenance, defect detection, energy, robotics Global DXC GSI Intelligent Boost AI and IoT Analytics Platform 5G Industrial Connectivity, Defect detection, OEE, safety, energy monitoring Global Innominds SI Intelligent Connected Edge Platform Predictive maintenance, AI on edge, asset tracking North America, EMEA Litmus Automation Industrial Connectivity, Industrial Data Ops Litmus Edge + Azure IoT Operations Edge Data, Smart manufacturing, IIoT deployments at scale Global, North America Mesh Systems GSI & ISV Azure IoT & Azure IoT Operations implementation services and solutions (including Azure IoT Operations-aligned connector patterns) Device connectivity and management, data platforms, visualization, AI agents, and security North America, EMEA Nortal GSI Data-driven Industry Solutions IT/OT Connectivity, Unified Namespace, Digital Twins, Optimization, Edge, Industrial Data, Real‑Time Analytics & AI EMEA, North America & LATAM NVIDIA Technology Partner Accelerated AI Infrastructure; Open libraries, models, frameworks, and blueprints for AI development and deployment. Cross industry digitalization and AI development and deployment: Generative AI, Agentic AI, Physical AI, Robotics Global Oracle ISV Oracle Fusion Cloud SCM + Azure IoT Operations Real-time manufacturing Intelligence, AI powered insights, and automated production workflows Global Rockwell Automation Industrial Automation FactoryTalk Optix + Azure IoT Operations Factory modernization, visualization, edge orchestration, DataOps with connectivity context at scale, AI ops and services, physical equipment, MES Global Schneider Electric Industrial Automation Industrial Edge Physical equipment, Device modernization, energy, grid Global Siemens Industrial Automation & Software Industrial Edge + Azure IoT Operations reference architecture Industrial edge infrastructure at scale, OT/IT convergence, DataOps, Industrial AI suite, virtualized automation. Global Sight Machine ISV Integrated Industrial AI Stack Industrial AI, bottling, process optimization Global Softing Industrial Industrial Connectivity edgeConnector + Azure IoT Operations OT connectivity, multi-vendor PLC- and machine data integration, OPC UA information model deployment EMEA, Global TCS GSI Sensor to cloud intelligence Operations optimization, healthcare digital twin experiences, supply chain monitoring Global This Ecosystem Model enables Industrial AI solutions to scale through clear roles, respected boundaries and composable systems: Control systems continue to be driven by automation leaders Safety‑critical, deterministic control stays with industrial automation partners who manage real‑time operations and plant safety. Customers modernize analytics and AI while preserving uptime, reliability, and operational integrity. Data, AI, and analytics scale independently A consistent edge to cloud data plane supports cloud scale analytics and AI, accelerating insight delivery without entangling control systems or slowing operational change. This separation allows customers and software providers to build AI solutions on top of a stable, industrial‑grade data foundation without redefining control system responsibilities. Specialized partners align solutions across the estate Partners contribute focused expertise across connectivity, analytics, security, and operations, assembling solutions that reduce integration risk, shorten deployment cycles, and speed time to value across the operational estate. From vision to production Industrial AI at scale depends on turning operational data into trusted, contextualized intelligence safely, repeatably, and across the enterprise. This guide shows how industrial partners, aligned on a shared Azure foundation, create the data plane that enables AI solutions to succeed in production. When data is ready, intelligence scales. Call to action: Use this guide to identify the partners and capabilities that best align to your current Industrial AI needs and take the next step toward production‑ready outcomes on Azure.From Cloud to Chip: Building Smarter AI at the Edge with Windows AI PCs
As AI engineers, we’ve spent years optimizing models for the cloud, scaling inference, wrangling latency, and chasing compute across clusters. But the frontier is shifting. With the rise of Windows AI PCs and powerful local accelerators, the edge is no longer a constraint it’s now a canvas. Whether you're deploying vision models to industrial cameras, optimizing speech interfaces for offline assistants, or building privacy-preserving apps for healthcare, Edge AI is where real-world intelligence meets real-time performance. Why Edge AI, Why Now? Edge AI isn’t just about running models locally, it’s about rethinking the entire lifecycle: - Latency: Decisions in milliseconds, not round-trips to the cloud. - Privacy: Sensitive data stays on-device, enabling HIPAA/GDPR compliance. - Resilience: Offline-first apps that don’t break when the network does. - Cost: Reduced cloud compute and bandwidth overhead. With Windows AI PCs powered by Intel and Qualcomm NPUs and tools like ONNX Runtime, DirectML, and Olive, developers can now optimize and deploy models with unprecedented efficiency. What You’ll Learn in Edge AI for Beginners The Edge AI for Beginners curriculum is a hands-on, open-source guide designed for engineers ready to move from theory to deployment. Multi-Language Support This content is available in over 48 languages, so you can read and study in your native language. What You'll Master This course takes you from fundamental concepts to production-ready implementations, covering: Small Language Models (SLMs) optimized for edge deployment Hardware-aware optimization across diverse platforms Real-time inference with privacy-preserving capabilities Production deployment strategies for enterprise applications Why EdgeAI Matters Edge AI represents a paradigm shift that addresses critical modern challenges: Privacy & Security: Process sensitive data locally without cloud exposure Real-time Performance: Eliminate network latency for time-critical applications Cost Efficiency: Reduce bandwidth and cloud computing expenses Resilient Operations: Maintain functionality during network outages Regulatory Compliance: Meet data sovereignty requirements Edge AI Edge AI refers to running AI algorithms and language models locally on hardware, close to where data is generated without relying on cloud resources for inference. It reduces latency, enhances privacy, and enables real-time decision-making. Core Principles: On-device inference: AI models run on edge devices (phones, routers, microcontrollers, industrial PCs) Offline capability: Functions without persistent internet connectivity Low latency: Immediate responses suited for real-time systems Data sovereignty: Keeps sensitive data local, improving security and compliance Small Language Models (SLMs) SLMs like Phi-4, Mistral-7B, Qwen and Gemma are optimized versions of larger LLMs, trained or distilled for: Reduced memory footprint: Efficient use of limited edge device memory Lower compute demand: Optimized for CPU and edge GPU performance Faster startup times: Quick initialization for responsive applications They unlock powerful NLP capabilities while meeting the constraints of: Embedded systems: IoT devices and industrial controllers Mobile devices: Smartphones and tablets with offline capabilities IoT Devices: Sensors and smart devices with limited resources Edge servers: Local processing units with limited GPU resources Personal Computers: Desktop and laptop deployment scenarios Course Modules & Navigation Course duration. 10 hours of content Module Topic Focus Area Key Content Level Duration 📖 00 Introduction to EdgeAI Foundation & Context EdgeAI Overview • Industry Applications • SLM Introduction • Learning Objectives Beginner 1-2 hrs 📚 01 EdgeAI Fundamentals Cloud vs Edge AI comparison EdgeAI Fundamentals • Real World Case Studies • Implementation Guide • Edge Deployment Beginner 3-4 hrs 🧠 02 SLM Model Foundations Model families & architecture Phi Family • Qwen Family • Gemma Family • BitNET • μModel • Phi-Silica Beginner 4-5 hrs 🚀 03 SLM Deployment Practice Local & cloud deployment Advanced Learning • Local Environment • Cloud Deployment Intermediate 4-5 hrs ⚙️ 04 Model Optimization Toolkit Cross-platform optimization Introduction • Llama.cpp • Microsoft Olive • OpenVINO • Apple MLX • Workflow Synthesis Intermediate 5-6 hrs 🔧 05 SLMOps Production Production operations SLMOps Introduction • Model Distillation • Fine-tuning • Production Deployment Advanced 5-6 hrs 🤖 06 AI Agents & Function Calling Agent frameworks & MCP Agent Introduction • Function Calling • Model Context Protocol Advanced 4-5 hrs 💻 07 Platform Implementation Cross-platform samples AI Toolkit • Foundry Local • Windows Development Advanced 3-4 hrs 🏭 08 Foundry Local Toolkit Production-ready samples Sample applications (see details below) Expert 8-10 hrs Each module includes Jupyter notebooks, code samples, and deployment walkthroughs, perfect for engineers who learn by doing. Developer Highlights - 🔧 Olive: Microsoft's optimization toolchain for quantization, pruning, and acceleration. - 🧩 ONNX Runtime: Cross-platform inference engine with support for CPU, GPU, and NPU. - 🎮 DirectML: GPU-accelerated ML API for Windows, ideal for gaming and real-time apps. - 🖥️ Windows AI PCs: Devices with built-in NPUs for low-power, high-performance inference. Local AI: Beyond the Edge Local AI isn’t just about inference, it’s about autonomy. Imagine agents that: - Learn from local context - Adapt to user behavior - Respect privacy by design With tools like Agent Framework, Azure AI Foundry and Windows Copilot Studio, and Foundry Local developers can orchestrate local agents that blend LLMs, sensors, and user preferences, all without cloud dependency. Try It Yourself Ready to get started? Clone the Edge AI for Beginners GitHub repo, run the notebooks, and deploy your first model to a Windows AI PC or IoT devices Whether you're building smart kiosks, offline assistants, or industrial monitors, this curriculum gives you the scaffolding to go from prototype to production.Zigbee Dongle vs. Dedicated Gateway for Azure IoT: An Architecture Choice
Hello Azure IoT Community, I'm deep into the architecture phase of a large-scale smart building project, using Azure IoT Hub as our central command. We're incorporating numerous Zigbee-based sensors and actuators for energy and environment monitoring. A critical debate has emerged: should we rely on a centralized Zigbee USB dongle, or deploy distributed, dedicated Zigbee gateways like the OWON SEG-X5? This decision impacts system resilience, cloud integration efficiency, and long-term operational stability. The Core Trade-off: Simplicity vs. Resilience Option A: The Centralized Dongle Approach This model uses a USB dongle connected to a gateway server, which becomes the sole coordinator for the Zigbee network before relaying data to IoT Hub. The Appeal: Low initial cost and simplicity for prototyping. The Scalability Risk: This creates a Single Point of Failure (SPOF). If the host server needs maintenance or encounters an issue, the entire Zigbee network—and all dependent automations—go offline. For a commercial building, this is a critical operational risk. Option B: The Distributed Gateway Architecture This model employs dedicated, standalone Zigbee gateways (e.g., https://www.owon-smart.com/zigbee-gateway-zigbeeethernetble-seg-x5-product/) deployed across different zones or floors. Each forms its own robust mesh and connects directly to Azure IoT Hub. The Resilience Gain: Faults are isolated. One gateway’s maintenance affects only its zone. The Edge Intelligence Advantage: Modern gateways can process data and execute rules locally. For instance, a gateway can directly process inputs from a Zigbee Door/Window Sensor (DWS 312) and a Multi-Sensor (PIR 323) to trigger a local light switch, all without a round-trip to the cloud. This aligns perfectly with the Azure IoT Edge paradigm, ensuring responsiveness and offline operation. Streamlined Cloud Integration: Gateways like the SEG-X5 come with integrated MQTT API support, allowing them to send structured data directly to IoT Hub, simplifying device management and message routing in the cloud. A Practical Insight from an ODM Case Study Our experience as an IoT ODM manufacturer has shown this shift in practice. In a project akin to the Hotel Room Management case in our portfolio, the initial design using a central server with dongles presented reliability concerns. The final solution utilized distributed OWON SEG-X5 Zigbee Gateways in each hotel wing. These gateways managed all in-room devices—from Smart Sockets (WSP 406 series) and Light Switches (SLC series) to Thermostats (PCT 504)—locally. They used their MQTT API to send consolidated occupancy and energy data to the building's cloud platform (integrated with IoT Hub). The result was a system where guest room automation remained functional despite network fluctuations, and maintenance could be performed per wing without building-wide impact. Conclusion and Discussion For proof-of-concepts, dongles are sufficient. For production-grade, scalable deployments where uptime is critical, dedicated gateways provide the necessary architectural foundation. I'm keen to hear from the community: In your Azure IoT solutions, how have you integrated non-IP protocol devices like Zigbee? What strategies do you employ to balance edge processing with cloud analytics? For those using gateway architectures, how do you handle device provisioning and security at scale? If you're interested in the technical specifics of how Zigbee gateways interface with cloud platforms, including API structures and network design considerations, we've elaborated on these topics in a technical overview on our site: [https://www.owon-smart.com/news/zigbee-dongles-vs-gateways-how-to-choose-the-right-network-coordinator/ ]. Looking forward to a fruitful discussion.Siemens and Microsoft: Beyond Connectivity to Autonomous, Sustainable Manufacturing
Explore how Siemens Industrial Edge and Microsoft Azure IoT Operations enable secure edge-to-cloud integration, contextualized data, and AI-driven insights—transforming factories into adaptive, future-ready operations.
Azure IoT Hub with ADR (preview): Extending Azure capabilities and certificate management to IoT
Operational excellence in every industry begins by linking the physical world to the digital, enabling organizations to turn raw data from connected assets into actionable insights and real-world improvements. Azure IoT Hub and Azure IoT Operations make this possible by seamlessly integrating data from machines whether on a single factory floor or spread across the globe into a unified platform. Together, they serve as the backbone of connected operations, ensuring that assets, sensors this data is then moved to Microsoft Fabric for real-time analytics and further leveraged by AI agents to drive informed decisions. This approach lets organizations scale efficiently, unifying teams, sites, and systems under the Adaptive Cloud Strategy. It enables use of cloud-native and AI technologies across hybrid, multi-cloud, edge, and IoT environments in a single operational model. Azure IoT Hub empowers organizations to securely and reliably manage connected assets across the globe, providing real-time visibility and control over diverse operations. With proven scalability, broad device support, and robust management tools, IoT Hub delivers a unified platform for developing and operating IoT solutions. Organizations in various industries are using Azure IoT Hub to enhance their operations. In mining, sensors provide real-time safety data and support compliance. Fleet managers track equipment health to boost efficiency and prevent failures, while rail operators use GPS and vibration sensors for precise monitoring and issue detection. Ports utilize conveyor and loading system metrics to optimize scheduling and reduce delays. These examples show how Azure IoT Hub delivers actionable insights, greater safety, and operational efficiency through connected devices. As customers evolve, Azure IoT Hub continues to advance, deepening its integration with the Azure ecosystem and enabling AI-driven, connected operations for the next generation of applications. Today, we’re announcing the public preview of Azure IoT Hub integration with Azure Device Registry bringing IoT devices under the purview of Azure management plane via ARM resource representation and securing them with best-in-class Microsoft-backed X.509 certificate management capabilities. From Connected Devices to Connected Operations Ready-to-use AI platforms are enabling organizations to unlock untapped operational data and gain deeper insights. Organizations are leveraging AI to unify machine and enterprise data, extract actionable insights, and translate them into measurable business gains. They are broadly transitioning from connected devices that simply gather and transmit telemetry, to connected operations which empower supervisors and AI agents to interpret events and respond to scenarios in real time. The integration of Azure IoT Hub with ADR enhancements extends the comprehensive capabilities of Azure to IoT devices. With this integration, Azure Device Registry (ADR) acts as the unified control plane for managing both physical assets from Azure IoT Operations and devices from Azure IoT Hub. It provides a centralized registry, ensuring every entity whether an industrial asset or a connected device is uniquely represented and managed throughout its lifecycle. By integrating with Azure IoT Hub, ADR enables consistent device onboarding, certificate management, and operational visibility at scale. This integration simplifies large-scale IoT fleet management and supports compliance and auditability across diverse deployments. What’s New in this Preview We’re excited to announce the public preview of new capabilities that bring IoT devices into the broader Azure ecosystem. This integration allows IoT to be managed at scale through the Azure management plane. It also strengthens security and enables consistent governance across large deployments: Deep integration with Azure: The Azure Device Registry (ADR) now offers a unified control plane, simplifying identity, security, and policy management for millions of devices. New ADR features make it easier to register, classify, and monitor devices, supporting consistent governance and better operational insights. Combined with Device Provisioning Service (DPS), these enhancements help reduce deployment challenges, speed up time-to-value, and lower operational risks. With IoT Hub integration, IoT Hub devices are represented as Azure resources, providing: One unified registry across multiple IoT Hubs and Azure IoT Operations (AIO) instances. ARM-based management for all Azure resources from cloud to edge. A consolidated view of the entire IoT fleet, simplifying large-scale deployments, monitoring and management. Certificate lifecycle management: Now in public preview, this capability enables secure onboarding and automated certificate rotation for IoT devices, directly integrated with ADR and IoT Hub. X.509 certificates are widely recognized for providing a robust security posture by establishing trusted, cryptographically verifiable device identities. Starting today, customers can use a Microsoft-backed PKI to issue X.509 certificates across their IoT fleets. Devices receive operational certificates that authenticate with IoT Hub, chained to Certificate Authorities (CAs). Policy-driven lifecycle management makes certificate renewal simpler and keeps state in sync with your Hubs. This integration sets the stage for Physical AI by connecting digital and physical systems, thus unlocking new possibilities for data and artificial intelligence. Customer feedback from Private Preview This release has received positive feedback from private preview customers. Particularly the Microsoft-supported PKI and certificate management capabilities, highlighting that previous manual processes were inefficient and fragmented. Customers further noted the advantages of grouping devices from multiple IoT Hubs under a unified namespace, which streamlined management. Moreover, the integration of certificate management within ADR has diminished the reliance on custom solutions. “We were genuinely impressed by how seamless it was to implement. With just a few clicks, clear policy definitions, and two calls in firmware, the entire process became automated, frictionless, and reliable with no external dependencies.” – Uriel Kluk, CTO, Mesh Systems Why It Matters These investments make Azure IoT Hub the cornerstone for connected operations at scale, empowering customers to: Reduce manual cert ops with policy‑driven rotation (fewer outages due to expired certs). Consolidate device registry in ADR for cross‑hub fleet governance. Accelerate compliance audits with centralized certificate lineage. Apply advanced AI tooling for predictive insights and automation. Call to Action Explore the new capabilities in public preview today and start building the next generation of connected operations with Azure IoT Hub and ADR. Learn more on Azure IoT Hub documentation
Bridging the Digital and Physical Worlds with Azure IoT Hub and Azure IoT Operations
Operational excellence starts with people. Empowering those people with the most up to date insights and recommendations requires bridging the gap between the physical and digital worlds to generate the best possible outcomes for real time decision making. Creating this bridge transforms data into insights, insights into intelligent actions, and actions into real-world results. Digital Operations, integrated with AI insights, help make this possible by combining data from connected assets across a variety of physical locations and deployment topologies, and transforming that data into insights and decisions that scale using AI and Analytics. At Microsoft Ignite, we’re extending this vision with new Azure IoT Hub and Azure IoT Operations capabilities to manage connected assets at scale, unify digital operations, and realize AI-enabled outcomes across your enterprise. Connected Operations in Action Azure IoT Hub and Azure IoT Operations form the backbone of connected operations, where every asset, sensor, and system contributes to a continuous loop of intelligence by moving data to Microsoft Fabric for real-time analytics, and for use with AI agents. This pattern applies to nearly every sector of the economy. In manufacturing, these capabilities allow production engineers to predict and avoid equipment failures by analyzing vibration and temperature data at the edge before costly downtime occurs. In energy and utilities, distributed sensors can provide data to control points that help balance load, optimize grid efficiency, and ensure safe operations even in remote areas. In transportation and logistics, connected fleets use edge AI models to detect safety risks in real time, while cloud-based analytics optimize routing and fuel efficiency across entire regions. Across industries, this edge-to-cloud collaboration enables the ability for intelligent systems to sense, reason, and act in the physical world with speed, safety, and precision. From Data to Intelligent Action Organizations today must capture and act on data from both geographically dispersed and tightly collocated assets. That data needs to be processed close to where it’s generated, at the edge, to enable real-time decision-making, reduce latency, and enhance security. At the same time, the cloud remains vital for contextualizing operational data with enterprise systems, training AI models, and managing a consistent identity and security framework across all assets. AI models trained in the cloud can then be deployed back to the edge, where they act on events in real time. Operators can work with AI agents to reason over this data whether it’s structured or unstructured, organized in silos, or contained in free-text fields, to provide results to a mixed team of human and AI operational assets. We have a portfolio of products uniquely designed to make this continuum, from edge to cloud, more intelligent, secure, and repeatable. Together with our partners, we help bridge Operational Technology (OT) with Information Technology (IT) to deliver better business outcomes. New at Ignite: Accelerating Digital Operations We’re excited to share our latest set of investments at Ignite across our portfolio of services. A few key announcements: Azure IoT Hub New Features (Preview): Simplifying Secure Connectivity at Scale Azure IoT Hub empowers organizations to securely and reliably manage connected assets across the globe, providing real-time visibility and control over diverse operations. With proven scalability, broad device support, and robust management tools, IoT Hub delivers a unified platform for developing and operating IoT solutions. As customers evolve, Azure IoT Hub continues to advance, deepening its integration with the Azure ecosystem and enabling AI-driven, connected operations for the next generation of applications. The next generation of Azure IoT Hub investments makes it easier and more secure than ever to connect and manage distributed assets. At Ignite, we’re previewing: New certificate management capabilities that simplify device onboarding and lifecycle management. Integration with Azure Device Registry (ADR) that brings all devices into a common control plane, enabling unified identity, security, and policy management. ADR enhancements that make it easier to register, classify, and monitor assets, paving the way for consistent governance and operational insight across millions of devices. This deeper Azure integration with ADR standardizes operations, simplifies oversight of edge portfolios including IoT devices, and brings the full power of Azure’s management ecosystem to IoT and Digital Operations workloads. Azure IoT Operations New Features (GA): The Foundation for AI in the Physical World Azure IoT Operations is more than an edge-to-cloud data plane, it’s the foundation for achieving AI in the physical world, enabling intelligent operational systems that can perceive, reason, and act to drive new operational efficiencies. Built on Arc-enabled Kubernetes, Azure IoT Operations unifies operational and business data across distributed environments, eliminating silos and providing a repeatable, scalable foundation for autonomous, adaptive operations. By extending familiar Azure management concepts to physical sites, Azure IoT Operations creates an AI-ready infrastructure that supports autonomous, adaptive operations at scale. Our latest GA release of Azure IoT Operations introduced major enhancements: Wasm-powered data graphs deliver fast, modular analytics helping businesses make near real-time decisions at the edge. Expanded connectors now include OPC UA, ONVIF, REST/HTTP, Server-Sent Events (SSE), and direct MQTT for richer industrial and IT integrations. OpenTelemetry (OTel) endpoint support enables seamless telemetry pipelines and observability. Asset health monitoring to provide unprecedented visibility and control. These capabilities help bridge Information Technology, Operational Technology, and data domains, empowering customers to discover, collect, process, and send data using open standards while laying the groundwork for self-optimizing environments where AI agents and human supervisors collaborate seamlessly. Integration with Fabric IQ and Digital Twin Builder To fully unlock the value of connected data, organizations need to contextualize it, linking operational signals to business meaning. Fabric IQ, a new offering announced at Ignite, and Digital Twin Builder in Fabric make this possible, transforming raw telemetry into AI-ready context. This integration allows companies to model complex systems, run simulations, and create intelligent feedback loops across manufacturing, logistics, and energy environments. Edge AI: Real-Time Intelligence in the Physical World Azure’s AI capabilities for edge environments bring intelligence closer to where it matters most. And, because these services are Arc-enabled, organizations can develop, manage and scale AI workloads across diverse environments using consistent tooling. Today, we are announcing updates to two of our key services that enable AI at the edge: Live Video Analysis features (Public Preview) in Azure AI Video Indexer enabled by Arc: delivers real-time agentic video intelligence to improve safety, quality, and operations. Edge RAG (Retrieval Augmented Generation) Public Preview Refresh enables local generative AI reasoning with contextual awareness - empowering AI agents to act within industrial constraints securely and efficiently. These innovations accelerate time to insight and help organizations deploy AI where milliseconds matter. Partner Innovation: Scaling Real Business Value Last year, we showcased the breadth of Azure IoT Operations’ industrial ecosystem. This year, we’re celebrating how partners are integrating, co-innovating, and scaling real customer outcomes. Our partners are packaging repeatable, scalable solutions that connect operational data to enterprise systems—enabling AI-driven insights and automation across sites, regions, and industries. At this year’s Ignite, we’re highlighting some great new partner innovations: NVIDIA is working with Microsoft to enable factory digital twins using the OpenUSD standard Siemens is enabling adaptive production through AI- and digital-twin-powered solutions supported by the integration of Siemens Industrial Edge with Azure IoT Operations Litmus Edge integrates with Azure IoT Operations via the Akri framework to automatically discover industrial devices, enable secure data flows, and support Arc-enabled deployment. Rockwell Automation is streamlining edge-to-cloud integration with its FactoryTalk Optix platform by delivering contextualized, AI-ready data seamlessly within Microsoft Azure IoT Operations architectures. Sight Machine is driving advanced analytics for quality and efficiency across multi-site operations. Through initiatives like Akri, Co-Innovate, and Co-Sell Readiness, our ecosystem is developing managed applications, packaged solutions, and marketplace offerings that accelerate deployment and unlock new revenue streams. These collaborations show how Azure IoT Operations is not just a platform, but a growth engine for industrial transformation. The Path Forward With these advancements, we’re helping organizations bring AI to the physical world by turning data into intelligence and intelligence into action. Customers like Chevron and Husqvarna are scaling beyond initial pilots, expanding their deployments from single-site to multi-site rollouts, unlocking new use cases from predictive maintenance to worker safety, and proving how adaptive cloud architectures deliver measurable impact across global operations. By connecting assets, empowering partners, and delivering open, scalable platform solutions, Microsoft is helping industries achieve resilient, adaptive operations that drive measurable business value. The digital and physical worlds are coming together with solutions that are secure, observable, AI-ready, and built to scale from a single site to global operations. Together, we’re creating a smarter, more connected future. Learn More Learn more about Azure IoT Hub and Azure IoT Operations here: Azure IoT – Internet of Things Platform | Microsoft Azure Learn more about new IoT Hub public preview features here: Azure IoT Hub documentation Discover Partner Solutions: Learn how Litmus and Sight Machine are advancing industrial analytics and integration with Azure IoT Operations. Explore Rockwell Automation and Siemens for more on adaptive cloud architectures and shop floor intelligence. Going to Ignite? If you’re at Ignite this week, you can learn more about how Microsoft enables Industrial Transformation at the following sessions: The New Industrial Frontier Reshaping Digital Operations with AI from Cloud and Edge Or come visit us on the show floor at the Azure Arc Expert Meet Up Focus Area in the Cloud and AI Platforms neighborhoodAzure IoT Operations 2510 Now Generally Available
Introduction We’re thrilled to announce the general availability of Azure IoT Operations 2510, the latest evolution of the adaptive cloud approach for AI in industrial and large scale commercial IoT. With this release, organizations can unlock new levels of scalability, security, and interoperability, empowering teams to seamlessly connect, manage, and analyze data from edge to cloud. What is Azure IoT Operations? Azure IoT Operations is more than an edge-to-cloud data plane, it’s the foundation for AI in physical environments, enabling intelligent systems to perceive, reason, and act in the real world. Built on Arc-enabled Kubernetes clusters, Azure IoT Operations unifies operational and business data across distributed environments, eliminating silos and delivering repeatability and scalability. By extending familiar Azure management concepts to physical sites, AIO creates an AI-ready infrastructure that supports autonomous, adaptive operations at scale. This approach bridges information technology (IT), operational technology (OT), and data domains, empowering customers to discover, collect, process, and send data using open standards while laying the groundwork for self-optimizing environments where AI agents and human supervisors collaborate seamlessly. We've put together a quick demo video showcasing the key features of this 2510 release. Watch below to discover how Azure IoT Operations' modular and scalable data services empowers IT, OT and developers. What’s New in Azure IoT Operations 2510? Management actions: Powerful management actions put you in control of processes and asset configurations, making operations simpler and smarter. Web Assembly (Wasm) data graphs: Wasm-powered data graphs for advanced edge processing, delivering fast, modular analytics and business logic right where your data lives. New connectors: Expanded connector options now include OPC UA, ONVIF, Media, REST/HTTP, and Server-Sent Events (SSE), opening the door to richer integrations across diverse industrial and IT systems. OpenTelemetry (OTel) endpoints: Data flows now support sending data directly to OpenTelemetry collectors, integrating device and system telemetry into your existing observability infrastructure. Improved observability: Real-time health status for assets gives you unmatched visibility and confidence in your IoT ecosystem. Reusable Connector templates: Streamline connector configuration and deployment across clusters. Device support in Azure Device Registry: Azure Device Registry (ADR) now treats devices as first‑class resources within ADR namespaces, enabling logical isolation and role‑based access control at scale. Automatic device and asset discovery and onboarding: Akri‑powered discovery continuously detects devices and industrial assets on the network, then automatically provisions and onboards them (including creating the right connector instances) so telemetry starts flowing with minimal manual setup. MQTT Data Persistence: Data can now be persisted to disk, ensuring durability across broker restarts. X.509 Auth in MQTT broker: The broker now supports X.509 authentication backed by Azure's Device Registry. Flexible RBAC: Built-in roles and custom role definitions to simplify and secure access management for AIO resources. Customers and partners Chevron, through its Facilities and Operations of the Future initiative, deployed Azure IoT Operations with Azure Arc to manage edge-to-cloud workloads across remote oil and gas sites. With a single management plane, the strategy unifies control over thousands of distributed sensors, cameras, robots, and drones. Real-time monitoring and AI enabled anomaly detection not only to enhance operational efficiency but also significantly improve worker safety by reducing routine inspections and enabling remote issue mitigation. This reuse of a global, AI-ready architecture positions Chevron to deliver more reliable, cleaner energy. [microsoft.com] Husqvarna implemented Azure IoT Operations across its global manufacturing network as part of a comprehensive strategy. This adaptive cloud approach integrates cloud, on-premises, and edge systems, preserves legacy investments, and enables real-time edge analytics. The result: data operationalization is 98% faster, imaging costs were slashed by half, productivity was improved, and downtime was reduced. Additionally, AI-driven capabilities like the Factory Companion powered by Azure AI empower technicians with instant, data-informed troubleshooting, shifting maintenance from reactive to predictive across sites. [microsoft.com] Together, these success stories show how Azure IoT Operations, combined with capabilities like Azure Arc, can empower industrial leaders to advance from siloed operations to unified, intelligent systems that boost efficiency, safety, and innovation. Additionally, this year we are celebrating how our partners are integrating, co-innovating, and scaling real customer outcomes. You can learn more about our partner successes at https://aka.ms/Ignite25/DigitalOperationsBlog. Learn more at our launch event Join us at Microsoft Ignite to dive deeper into the latest innovations in Azure IoT Operations 2510. Our sessions will showcase real-world demos plus expert insights on how new capabilities accelerate industrial transformation. Don’t miss the chance to connect with product engineers, explore solution blueprints, and see how Azure IoT Operations lays the foundation for building and scaling physical AI. Get Started Ready to experience the new capabilities in Azure IoT Operations 2510? Explore the latest documentation and quickstart guides at https://aka.ms/AzureIoTOperations Connect with the Azure IoT Tech Community to share feedback and learn from peers.Understanding Small Language Modes
In Part 1, we discussed the differences between Large Language Models (LLMs) and Small Language Models (SLMs), highlighting how SLMs are transforming edge computing. Unlike LLMs, which rely on massive cloud infrastructure, SLMs can run directly on local devices like smartphones and IoT systems, offering faster performance, better privacy, and lower energy use. In this second part, we dive into how SLMs work, exploring their technical foundations and design principles that make them ideal for edge environments.Solving the Data Challenge for Manufacturers with Sight Machine & Azure IoT Operations
Delivering Industrial AI: From Data to Results As manufacturers accelerate their digital transformation, the ability to unify and leverage operational data is the difference between incremental improvement and competitive advantage. Today, we’re launching a joint solution with Sight Machine, purpose-built to solve the OT data challenge and deliver the full Industrial AI stack in weeks, not months: Sight Machine and Microsoft Integrated Industrial AI Stack on Azure This offering is proven in the field, already driving measurable productivity gains for customers in automotive, food, and other sectors with rapid POC cycles and commercial-scale deployments. By integrating Sight Machine’s industrial AI platform with Azure IoT Operations and Microsoft Fabric, we standardize and contextualize machine data at scale, enabling analytics, automation, and actionable insights across the enterprise. What Sets This Solution Apart Fast Deployment: Get the full Industrial AI stack up and running in weeks, not months. End-to-End Integration: Sight Machine’s industrial AI platform works seamlessly with Azure IoT Operations and Microsoft Fabric, standardizing OT data for enterprise-wide use. Real Results: Customers in automotive, food, and other industries are already seeing measurable productivity gains and faster decision cycles. Scalable & Secure: Built on Azure’s adaptive cloud and zero-trust security, with SI partners ready to support commercial scale. Delivering a unified Industrial AI stack Today marks a pivotal moment for manufacturers: the launch of a fully integrated Industrial AI solution, jointly delivered by Microsoft and Sight Machine. This offering brings together the entire Industrial AI stack spanning cloud, edge, and on-premises, enabling organizations to unlock transformative business value. The integrated solution enables customers to transform data into business value by seamlessly contextualizing and moving data from the Edge using Sight Machine and Azure IoT Operations to Microsoft Fabric. Within Microsoft Fabric, the data can be further contextualized and enriched to support AI agents and can be extended to visualize 3D digital twins using NVIDIA Omniverse. The integrated solution has following key components: Azure IoT Operations Streams secure, real-time telemetry from industrial assets to the cloud, enabling visibility and control across edge and enterprise environments. Microsoft Fabric Provides a single analytics and governance platform, merging IT and OT data for enterprise-wide insights. Sight Machine Industrial AI Platform Refines data into “gold-level” quality, fully contextualized and structured for AI, predictive maintenance, and process optimization. M365 Copilot & Agentic Intelligence Surfaces actionable insights directly in familiar tools like Teams and Excel, empowering operators and managers to make informed decisions instantly. NVIDIA Omniverse Integration Extends capabilities into immersive 3D digital twins and physics-based simulations, enabling manufacturers to visualize live operations and test changes virtually before implementing them. Customer Impact Manufacturing is the world’s largest sector, generating twice as much data as any other industry. Yet, the complexity and fragmentation of OT (Operational Technology) data have long limited the adoption of AI at scale. Sight Machine solves this challenge by integrating with every level of the Azure stack, structuring raw OT data into high-quality, contextualized “gold” data, ready for advanced analytics and AI. This integrated offering removes barriers to AI adoption. Manufacturers can connect assets, contextualize data, and deliver actionable insights directly to teams, whether in Teams, Excel, or immersive 3D digital twins. The result: higher productivity, smarter operations, and continuous improvement. Take the Next Step Ready to accelerate your digital transformation? Explore the Sight Machine + Azure IoT Operations solution in the Marketplace. Start your journey to smarter manufacturing today: Sight Machine on Azure
