Model Mondays S2:E7 · AI-Assisted Azure Development
Welcome to Episode 7! This week, we explore how AI is transforming Azure development. We’ll break down two key tools—Azure MCP Server and GitHub Copilot for Azure—and see how they make working with Azure resources easier for everyone. We’ll also look at a real customer story from SightMachine, showing how AI streamlines manufacturing operations.
Azure IoT Operations MQTT Broker: Performance Benchmarking on Throughput and Latency
1. Introduction When deploying an MQTT broker in a production environment, understanding its performance characteristics is crucial. Whether you're handling IoT sensor data, real-time event streams, or enterprise messaging, knowing how the broker performs under load helps in optimizing deployments. In this post, we evaluate the performance of the Azure IoT Operations MQTT Broker (subsequently referred to as Broker for brevity), focusing on: Throughput – How many messages per second the broker can handle. Latency – The time taken for messages to travel from publishers to subscribers. All tests were conducted using MQTT QoS 1 to ensure consistent balance between reliability and throughput. By following a structured performance testing approach, we aim to provide insights into how the Broker scales and where potential bottlenecks may arise. 👉 If you're looking for a quick summary, jump to the Key Takeaways section below. 2. Test Setup For accurate benchmarking, we set up Standard_D4s_v5 virtual machines (VMs) to ensure consistent and efficient message handling. To replicate our performance results, use the same VM SKU and test configuration. 2. 1 Infrastructure configuration Hardware configuration VM Architecture: x64 VM Image: Ubuntu Server 22.04 LTS - x64 Gen2 VM SKU: Standard_D4s_v5 vCPUs: 4 Memory: 16 GiB RAM Networking: All VMs are within the same virtual network (VNet) to minimize latency and reduce external network delays Software configuration OS Flavor: Ubuntu Server 22.04 LTS Version: 22.04 LTS Kubernetes distribution: K3s Kubernetes version: v1.28.5 2.2 Azure IoT Operation configuration The Azure IoT Operations configuration defined below is optimized for performance testing and MUST NOT be used in production as TLS encryption, authentication, and diagnostics pods are disabled to reduce variability. The Broker consists of frontend and backend partitions for optimized message handling: This setup is optimized for a 5-node cluster, ensuring scalability, and redundancy. Broker Configuration: Frontend: 5 replicas Backend: 5 partitions Redundancy factor of 2 2 workers Note: Increased redundancy doubles CPU usage, and therefore it also reduces the total available CPU for performing the same workload, potentially impacting overall efficiency. Broker Listener: Configured with a Load Balancer port 1883 Broker Nodes: 5 x Azure D4s_v5 VMs (4 vCPUs, 16 GiB memory, Ubuntu 22.04) Client Node: 1 x Azure D16s v5 VM (16 vCPUs, 64 GiB memory for load testing) Note: A more powerful 8-core VM is recommended to prevent the client from becoming a bottleneck, as EMQTT-bench by EMQX has high CPU consumption. The broker configuration is available in Azure IoT Mqtt Optimization. Json 3. Methodology To evaluate the performance of IoT Operations MQTT broker we used emqtt-bench, an open source MQTT v5.0 benchmark tool designed by EMQX. For optimal performance during testing, the inflight queue should be configured to a minimum of 100. 3.1 Client Configuration For 5-node cluster testing, a dedicated high-performance VM is required to act as the client. This VM must be separate from the cluster to prevent resource contention, ensuring that benchmarking reflects the broker's actual optimal performance. 3.2 Understanding the Performance Metrics Maximum Throughput – Measures the highest number of messages per second the broker can process. Note: Optimal performance requires finding a balance—publishers should send messages fast enough to fully utilize subscribers without overwhelming them. Average Latency– The time, in milliseconds, it takes for a message to travel from a publisher to a subscriber. Message Size – Tested with 16 Bytes, 8 KB, and 255 KB payloads to understand size impact. Evaluates how different payload sizes impact throughput and latency. Data Throughput – Measures the total volume of data transmitted per second, expected in megabytes per second (MB/sec). 3.3 Test Scenarios We tested the broker under different conditions to observe how it handles increasing workloads: Varying Publisher Rates – Analyzing throughput changes with increasing message rates. Different Payload Sizes – Measuring the impact of small (16 B), medium (8 KB), and large (255 KB) payloads. Fan-In / Balanced / Fan-Out – Comparing multiple publishers to one subscriber (fan-in) vs. one publisher to many subscribers (fan-out) vs an equal number of publishers and subscribers (balanced). Publisher / Subscriber Configuration – Vary number of publishers and subscribers across the three scenarios. QoS - All tests were performed using MQTT QoS 1, which ensures at least once message delivery. This strikes a balance between reliability and performance, making it more representative of real-world production scenarios where message loss is unacceptable, but the overhead of QoS 2 is not justified. We measured broker efficiency using different payload sizes across different publisher-to-subscriber ratios. The Fan-In test evaluated performance with a high number of publishers sending messages to a single subscriber. The Fan-Out stress test analyzed message distribution from a limited number of publishers to many subscribers under high throughput conditions. The Balanced test simulated a mixed workload with equal publishers and subscribers. 4. Results Detailed Performance Metrics: Fan-In, Fan-Out, and Balanced Scenarios Scenario Configuration Payload Size Max Throughput (msg/sec) Data Throughput (MB/sec) Average Latency (ms) Workload Description Fan-In 1000 pub 1 sub 16 B 41,352 0.63 124 High-Load Fan-In 1000 pub 1 sub 8 KB 14,439 112.67 26 High-Load Fan-In 1000 pub 1 sub 255 KB 992 246.8 520 High-Load Fan-In Balanced 1 pub 1 sub 16 B 50,739 6.49 2 Balanced Mixed-Load 1 pub 1 sub 8 KB 9,500 77.8 10 Balanced Mixed-Load 1 pub 1 sub 255 KB 1,314 327.08 540 Balanced Mixed-Load 100 pub 100 sub 16 B 279,949 4.27 350 Balanced Mixed-Load 100 pub 100 sub 8 KB 34,193 266.95 139 Balanced Mixed-Load 100 pub 100 sub 255 KB 2,871 715.42 2,800 Balanced Mixed-Load Fan-Out 1 pub 1000 sub 16 B 42,000 0.64 4 Large-scale Broadcast Fan-Out 1 pub 1000 sub 8 KB 15,003 117.25 6 Large-scale Broadcast Fan-Out 1 pub 1000 sub 255 KB 1,000 249.86 130 Large-scale Broadcast Fan-Out 5. Key Takeaways Takeaway 1: Data Throughput Scales with Payload Size. Even though the number of messages per second drops with larger payloads, data throughput (MB/sec) increases significantly. For example: Fan-In at 255 KB: 246.8 MB/sec Balanced at 255 KB: 715.4 MB/sec Takeaway 2: Performs Best in Low-Latency Use Cases. When message sizes are small (e.g. 16 B, 8 KB) and the topology is lightweight (e.g. 1 pub to 1 sub), the broker achieves: Avg latency as low as 1-2 ms Throughput over 270,000 msg/sec (Balanced scenario at 16 B) Ideal low-latency use cases: Real-time control systems (e.g. robotic arm commands, PLC feedback loops) Smart home device synchronization Autonomous vehicle telemetry coordination Industrial automation events (e.g. triggers from sensors to actuators) For time-sensitive operations, our broker provides sub-10 ms latencies and massive message fanout capability, even under constrained payload sizes. Takeaway 3: Fan-In Saturates Faster Than Fan-Out In QoS 1 tests, we observed Fan-In topologies (1000 devices → 1 endpoint) hit latency walls earlier than Fan-Out topologies (1 device → 1000 endpoints), even with similar message throughput. Fan-In (8 KB): 14,439 msg/sec @ 26 ms latency Fan-Out (8 KB): 15,003 msg/sec @ only 6 ms latency What this shows: In Fan-In, the broker handles thousands of simultaneous inbound QoS 1 acknowledgments — creating coordination pressure. In Fan-Out, a single publisher sends at a controlled rate, making it easier for the broker to fan out efficiently. We designed our broker to sustain intelligent traffic shaping and are continuing to enhance its performance under Fan-In workloads where coordination pressure is highest. 6. Optimization Strategies The Azure IoT Operations MQTT broker is built to support scalable, high-throughput, and low-latency messaging. To harness its full potential across diverse workload patterns, optimization should focus on balanced resource utilization and minimizing message delivery bottlenecks. Maximize Throughput without overload Fan-Out scenarios achieved strong throughput with consistently low latency, even under high subscriber counts. While they didn’t reach the raw message rate of Balanced workloads, their efficiency under broadcast pressure makes them ideal for scenarios requiring timely delivery to many endpoints. Recommended Actions: Batch and Compress Messages: Reduces overhead, improving payload transmission rates. Balance Publish Load: Distribute publishers evenly across broker nodes to avoid overloading a single point of ingestion. Maintain Low Latency for Real-Time Use Cases The broker excels in low-latency performance for small payloads and tightly coupled pub-sub pairs — as seen in 1:1 scenarios with 16 B payloads achieving 2–6 ms latency. These characteristics are crucial for real-time, control-plane workloads. Recommended Actions: Use Smaller Payloads for Time-Sensitive Ops: Critical for scenarios like robotics, actuator control, or telemetry alerting. Load Balance Across Nodes: Adjust broker cardinality, including frontend replicas (for client connection distribution) and backend partitions (for message throughput scaling) to ensure even load distribution across nodes and optimal performance. Enable MQTT Persistent Sessions: Minimizes reconnection overhead for frequently offline clients, learn how to enable persistent sessions with Mosquitto CLI by setting -c and --session-expiry-interval Optimize Deployment Scale for Workload Demands Scalability depends on configuring the right cardinality — that is, the number of frontend replicas, backend partitions, and compute resources — to match your connection and throughput requirements. Recommended Actions: High Connection Load: Scale frontend replicas to match node count (e.g., 3 replicas for 3 nodes) to distribute client connections evenly. High Message Throughput: Increase backend partitions to parallelize message processing (e.g., start with 1 partition per node and scale as needed). Heavy Payload Scenarios: Allocate more memory and CPU to worker pods to avoid slowdowns from large payload serialization and transmission. Backend Resiliency: Ensure redundancyFactor remains at the default of 2 (or more) so that each partition has at least two replicas, enabling failover protection without additional configuration For detailed guidance on these optimizations, visit our documentation: Learn more → 7. Conclusion The Azure IoT Operations MQTT broker is engineered for high performance, scalability, and efficiency, as demonstrated through rigorous benchmarking. In high-throughput balanced configurations, it sustained up to 279,949 messages/sec with 16 B payloads, showcasing best-in-class throughput for high-volume, symmetric pub-sub workloads. For bandwidth-heavy use cases, the broker handled up to 715 MB/sec (255 KB payloads), proving its scalability for large data transfers. Balanced 1:1 scenario also delivered predictable low-latency performance, with average latencies as low as 2 ms, making them ideal for real-time messaging. Meanwhile, Fan-In configurations remain optimal for centralized data aggregation tasks like telemetry logging, handling tens of thousands of messages/secs with acceptable latency. To maximize performance, we recommend key optimization strategies including load balancing, latency reduction, and workload-specific tuning. These approaches ensure efficiency at scale—whether you're managing high connection loads, scaling throughput, or handling large payloads in real-world deployments. For in-depth configuration guidance, visit our documentation.Announcing the Firmware Analysis Public Preview
Consider an organization with thousands of smart sensors, IoT/OT and network equipment deployed on factory floors. Most of these devices are running full operating systems, but unlike traditional IT endpoints which often run security agents, IoT/OT and network devices frequently function as “black boxes”: you have little visibility into what software they’re running, which patches are applied, or what vulnerabilities might exist within them. This is the challenge many organizations face with IoT/OT and networking equipment - when a critical vulnerability is disclosed, how do you know which devices are at risk? To help address this challenge, we are excited to announce the public preview of firmware analysis, a new capability available through Azure Arc. This extends the firmware analysis feature we introduced in Microsoft Defender for IoT, making it available to a broader range of customers and scenarios through Azure. Our goal is to provide deeper visibility into IoT/OT and network devices by analyzing the foundational software (firmware) they run. Firmware analysis will also help companies that build firmware for devices better meet emerging cybersecurity regulations on their products. In this post, we’ll explain how the service works, its key features, and how it helps secure the sensors and edge devices that feed data into AI-driven industrial transformation. Securing Edge Devices to Power AI-Driven Industrial Transformation In modern industrial environments, data is king. Organizations are embracing Industry 4.0 and AI-driven solutions to optimize operations, leveraging advanced analytics and machine learning. The path to AI-driven industrial transformation is fueled by data – and much of that data comes from sensors and smart devices at the edge of the network. These edge devices measure temperature, pressure, vibration, and dozens of other parameters on the factory floor or in remote sites, feeding streams of information to cloud platforms where AI models turn data into insights. In fact, sensors are the frontline data collectors in systems like predictive maintenance, continuously monitoring equipment and generating the raw data that powers AI predictions. However, if those edge devices, sensors, and networking equipment are not secure and become compromised, the quality and reliability of the data (and thus the AI insights) cannot be guaranteed. Vulnerable devices can also be used by attackers to establish a foothold in the network, allowing them to move laterally to compromise other critical systems. In an industrial setting this could mean safety hazards, unplanned downtime, or costly inefficiencies. This is why securing the smart devices and networking equipment at the foundation of your industrial IoT data pipeline is so critical to digital transformation initiatives. By using firmware analysis on the devices’ firmware before deployment (and regularly as firmware updates roll out), the manufacturer and plant operators gain visibility into the security posture of their environment. For example, they might discover that a particular device model’s firmware contains an outdated open-source library with a known critical vulnerability. With that insight, they can work with the vendor to get a patched firmware update before any exploit occurs in the field. Or the analysis might reveal a hard-coded passwords for maintenance account in the device; the ops team can then ensure those credentials are changed or the device is isolated in a network segment with additional monitoring. In short, firmware analysis provides actionable intelligence to fortify each link in the chain of devices that your industrial systems depend on. The result is a more secure, resilient data foundation for your AI-driven transformation efforts – leading to reliable insights and safer, smarter operations on the plant floor. Firmware analysis is also a key tool used by device builders – by analyzing device firmware images before they are delivered to customers, builders can make sure that new releases and firmware updates meet their and their customers’ security standards. Firmware analysis is a key component to address emerging cybersecurity regulations such as the EU Cyber Resilience Act and the U.S. Cyber Trust Mark. How Firmware Analysis Works and Key Features Firmware analysis takes a binary firmware image (the low-level software running on an IoT/OT and network device) and conducts an automated security analysis. You can upload an unencrypted, embedded Linux-based firmware image to the firmware analysis portal. The service unpacks the image, inspects its file system, and identifies potential hidden threat vectors – all without needing any agent on the device. Here are the main capabilities of the firmware analysis service: Identifying software components and vulnerabilities: The first thing the analysis does is produce an inventory of software components found inside the firmware, generating a Software Bill of Materials (SBOM). This inventory focuses especially on open-source packages used in the firmware. Using this SBOM, the service then scans for known vulnerabilities by checking the identified components against public Common Vulnerabilities and Exposures (CVEs) databases. This surfaces any known security flaws in the device’s software stack, allowing device manufacturers and operators to prioritize patches for those issues. Analyzing binaries for security hardening: Beyond known vulnerabilities, our firmware analysis examines how the firmware’s binaries were built and whether they follow security best practices. For example, it checks for protections like stack canaries, ASLR (Address Space Layout Randomization), and other compile-time defenses. This “binary hardening” assessment indicates how resistant the device’s software might be to exploitation. If the firmware lacks certain protections, it suggests the device could be easier to exploit and highlights a need for improved secure development practices by the manufacturer. In short, this feature acts as a gauge of the device’s overall security hygiene in its compiled code. Finding weak credentials and embedded secrets: Another critical aspect of the analysis is identifying hard-coded user accounts or credentials in the firmware. Hard-coded or default passwords are a well-known weakness in IoT devices – for instance, the Mirai botnet famously leveraged a list of over 60 factory-default usernames and passwords to hijack IoT devices for DDoS attacks. Firmware analysis will flag any built-in user accounts and the password hash algorithms used, so manufacturers can remove or strengthen them, and enterprise security teams can avoid deploying devices with known default credentials. Additionally, the firmware analysis looks for cryptographic materials embedded in the image. It will detect things like expired or self-signed TLS/SSL certificates, which could jeopardize secure communications from a device. It also searches for any public or private cryptographic keys left inside the firmware – secrets that, if found by adversaries, could grant unauthorized access to the device or associated cloud services. By uncovering these hidden secrets, the service helps eliminate serious risks that might otherwise go unnoticed in the device’s software. All these insights – from software inventory and CVEs to hardening checks and secret material detection – are provided in a detailed report for each firmware image you analyze. Firmware analysis provides deep insights, clear visibility, and actionable intelligence into your devices' security posture, enabling you to confidently operate your industrial environments in the era of AI-driven industrial transformation. Getting Started and What’s Next If you have IoT/OT and network devices in your environment, use firmware analysis to test just how secure your devices are. Getting started is easy: access firmware analysis public preview by searching on “firmware analysis” in the Azure portal, or access using this link. In the future, firmware analysis will be more tightly integrated into the Azure portal. Onboard your subscription to the preview and then upload firmware images for analysis - here is a step-by-step tutorial. The service currently supports embedded Linux-based images up to 1GB in size. In this preview phase, there is no cost to analyze your firmware – our goal is to gather feedback. We are excited to share this capability with you, as it provides a powerful new tool for securing IoT/OT and network devices at scale. By shedding light on the hidden risks in device firmware, firmware analysis helps you protect the very devices that enable your AI and digital transformation initiatives. Firmware is no longer just low-level code—it’s a high-stakes surface for attack, and one that demands visibility and control. Firmware analysis equips security teams, engineers, and plant operators with the intelligence needed to act decisively—before vulnerabilities become headlines, and before attackers get a foothold. Please give the firmware analysis preview a try and let us know what you think.Microsoft and Siemens: Accelerating Digital Transformation Together
In the ever-evolving landscape of industrial manufacturing, the collaboration between Microsoft and Siemens marks a significant step towards achieving adaptive and integrated production systems. Leveraging the capabilities of Siemens Xcelerator and Microsoft’s adaptive cloud approach, this partnership aims to bridge the gap between operational technology (OT) and information technology (IT) to create a seamless, data-driven production environment. Breaking Down Silos with Edge-to-Cloud Integration The convergence of IT and OT environments is revolutionizing industrial data and workloads, enabling the creation of adaptive production systems that enhance efficiency, flexibility, and innovation. Edge computing plays a pivotal role in this transformation by capturing and processing data directly at the source. Siemens Industrial Edge seamlessly interfaces with Microsoft Azure IoT Operations, enabling manufacturers to integrate MQTT and OPC UA data flows from the Industrial Edge with Azure IoT Operations. This joint effort ensures continuous data flows from industrial assets to Azure IoT Operations, fostering an interoperable OT and IT data plane. A Collaborative Approach to Modern Manufacturing Siemens brings extensive expertise in factory automation and digital transformation to this collaboration. Siemens Industrial Edge facilitates the deployment and management of workloads and connectivity applications, seamlessly connecting industrial assets to the cloud. This powerful OT data plane, provided by Siemens Industrial Edge, addresses mission-critical production applications such as virtualized control, low-latency closed-loop AI, executable digital twins, and production line-level analytics. Azure’s adaptive cloud approach integrates teams, sites, and systems into a unified model for operations, security, applications, and data across hybrid, multicloud, edge, and IoT environments. This approach ensures that all aspects of an organization's digital infrastructure work together, enhancing efficiency and collaboration. Azure IoT Operations, a component of this adaptive cloud approach, provides tools and infrastructure to connect edge devices while integrating data, enabling organizations to optimize their operations and utilize the potential of their IoT environments. Driving Digital Transformation Together As manufacturers face increasingly challenging conditions, such as scarce resources and volatile supply chains, the need for adaptive and scalable production systems has never been greater. Siemens and Microsoft are committed to reducing the complexity of integrating and managing infrastructure, data, and applications. This collaboration enables manufacturers to accelerate their digital transformation, moving from automated to adaptive production systems. Harnessing AI for Enhanced Production The partnership between Microsoft and Siemens empowers manufacturers to leverage AI to improve machine performance, product quality, and operational efficiency. By utilizing the Siemens Industrial AI portfolio alongside Azure Machine Learning services, manufacturers can train AI models in the cloud and deploy them at the edge with low latency. This capability allows for real-time insights and decision-making, enhancing overall equipment efficiency and reducing manual rework and costs. The Road Ahead The partnership between Siemens and Microsoft represents a significant milestone in the journey towards digital transformation. By providing a seamless data flow from the shopfloor to the cloud, this collaboration empowers manufacturers to harness advanced technologies such as AI and digital twins to streamline their production processes. As both companies continue to innovate and expand their capabilities, the future of manufacturing looks brighter than ever. For further details on Microsoft’s adaptive cloud approach, visit https://azure.microsoft.com/en-us/solutions/adaptive-cloud. You can also visit the Microsoft booth at Hannover Messe 2025 in Hall 017, Booth G06 to learn more and see the adaptive cloud approach and Azure IoT Operations in action. For more information on Siemens Industrial Edge, visit Siemens Xcelerator: Siemens accelerates IT and OT integration with Microsoft for Edge, Cloud, AI and Simulation | Press | Company | SiemensScaling industrial transformation with a robust partner ecosystem
In recent years, manufacturers have been on a journey to incorporate intelligent technologies like AI into their business processes. These exciting advancements are happening within an extended ecosystem, encompassing everything from planning and manufacturing to distribution and servicing of goods. A defining aspect of many such business processes is their continuous generation of data, which, when effectively contextualized and analyzed, can unlock critical business outcomes, including minimizing downtime, reducing waste, enhancing quality, improving sustainability, and boosting worker productivity. In addition to analytics, a comprehensive data governance strategy is fundamental as it supports the ability to embrace ecosystem-driven collaboration, a key component to unlock the full potential of AI-driven manufacturing. Challenges in meeting the promise of IT and OT integration With AI only as good as the data behind it, the ability to harness data across an ecosystem is paramount. However, the inherent complexities within industrial environments create digital transformation barriers. Each factory has its own unique mix of automation equipment and software configurations based on site-specific production processes. Management and data handling are also system and site specific. When organizations try to scale transformation efforts across different sites, these complexities multiply, with individual IT management systems adding permutations. Due to the variety of source and configuration combinations, pulling the right data, semantics, and contextualization into an external analysis platform becomes incredibly difficult and cost prohibitive. As a result, the ability to scale an outcome through the use of a digital feedback loop is completely out of reach. How an adaptive cloud approach supports operational transformation To overcome these challenges, organizations can benefit from a consistent approach to industrial data value realization that is repeatable across sites. Azure’s adaptive cloud approach enables organizations to secure, manage, and scale industrial operations by unifying data, applications, and infrastructure across edge and cloud environments. By leveraging the adaptive cloud approach, businesses can create a unified data foundation, breaking down operational silos to drive AI-driven insights and improved collaboration between IT and OT teams. Azure IoT Operations, enabled by Arc empowers customers to easily move machine and process data between the edge and cloud in a highly unified and repeatable way. Under the hood, Azure IoT Operations is a full-stack data plane that runs in on-premises Arc-enabled Kubernetes clusters. It enables customers to discover Assets via Akri and collect data. Then, customers can process and send data from the edge to the cloud using open standards and open protocols that are managed and supported by Microsoft. This solution helps enable unified data flow from facilities to natively integrated cloud destinations, including Microsoft Fabric, Azure Event Hubs, and Azure Event Grid's MQTT broker which provides real-time insights and AI-driven decision-making. Azure IoT Operations leverages Azure Arc to extend the cloud management pattern down to the physical site, using the same cloud deployment and management controls as Azure to enable unique advantages in repeatability and scalability across the enterprise. While Azure's adaptive cloud approach can provide a foundation to simplify everything from data collection to scaling AI initiatives, Microsoft is a platform company, and our partners are essential to success in the complex industrial market. Why a partner ecosystem is critical for enabling customer success Achieving business outcomes from industrial data requires navigating the complexity of interconnected technology landscapes, where diverse technologies and systems must cohesively integrate. The siloed IT, OT, and ET data that results from these diverse systems can slow AI adoption, limiting manufacturers’ ability to extract real-time insights. A collaborative vendor network can help address these challenges by enabling streamlined data exchange, enhanced automation, and increased operational intelligence. The transformation enabled by this network demands a collective approach, bringing together industrial automation partners offering industry-specific AI and analytics solutions, system integrators collaboratively engineering IT-OT solutions, OEMs modernizing production lines, and ISVs to develop industry-specific solutions that drive efficiency and scalability. A multi-cloud, open, and interoperable approach can allow businesses to connect engineering, production, and supply chain workflows into AI-driven digital infrastructure from cloud to edge. Manufacturers operate in complex multi-vendor environments that demand flexibility and interoperability. Choosing to adopt an open and collaborative partner network approach offers the opportunity to extend the life of investments and adopt AI and automation gradually. In addition, unlike closed models that often lead to vendor lock in, open ecosystems enhance security and governance through consistent policy enforcement, interoperability, and real-time visibility across multi-cloud, edge, and on-prem environments. For instance, a solution like Azure Arc offers centralized security controls, automated compliance and third-party tool integration. Industrial enterprises desire a unified, scalable AI-cloud-edge strategy to optimize engineering, production, and supply chain workflows. To make outcomes from Industrial AI initiatives a reality, organizations — including traditional competitors —should consider embracing partnerships, open standards and an adaptive cloud approach to enable easier connectivity and interoperability. Microsoft’s open, scalable, and multi-cloud ecosystem helps enable more efficient integration of Azure solutions with third-party platforms (public and private clouds) and open industry standards that enable data interoperability across IoT, AI, and automation solutions. Learn more about how Microsoft, along with partners, is reimagining how intelligent digital threads and AI agents will transform the manufacturing industry here. Join Us - Industrial AI in Action at Hannover Messe 2025 Join us at the Microsoft booth in Digital Ecosystems Hall 17 to explore the latest innovations in our partner ecosystem supporting the transformation of industrial operations. Experience live demonstrations showcasing how AI-driven manufacturing, real-time data insights, and an adaptive cloud approach drive efficiency, flexibility, and innovation. See firsthand how Microsoft and its partners mentioned below are enabling intelligent automation, predictive quality control, and improved IT/OT integration to accelerate digital transformation. Avanade Avanade excels in IT/OT integration and advanced manufacturing solutions, with specialized expertise in integrating PLM, ERP, and MES systems for digital continuity across design, manufacturing, supply chain, and service processes. Avanade offers dynamic sourcing for flexible procurement and supplier collaboration, process flexibility for diverse product variants, and human-machine collaboration to meet new product requirements. At HMI 2025, Avanade and Microsoft will showcase advanced closed-loop manufacturing demos using AI machine vision for quality control, integrated with Azure IoT Operations—which leverages MQTT and OPC UA protocols to streamline data transport and connectivity. Visit the Microsoft booth to explore how seamless system integration, dynamic sourcing, and human-machine collaboration can help produce superior products faster with less waste. Learn more about Avanade at HMI 2025. Capgemini Microsoft and Capgemini are driving the next era of smart manufacturing by embracing the adaptive cloud approach to accelerate digital transformation. Through Capgemini’s Intelligent Industry offerings worker performance and operational efficiencies can be improved through AI-driven processes—empowering manufacturers to move beyond manual workflows and unlock new levels of productivity. Capgemini integrates across edge to cloud environments using services like Azure IoT Operations, Azure AI, and Microsoft Fabric to optimize quality, and overall equipment effectiveness (OEE) for manufacturers. Join us at Capgemini’s Theatre Talk at HMI on Thursday, April 3 at 10:00am, where industry leaders will share how AI, when paired with edge to cloud technologies, can unlock the full potential of smart factories. Be part of the conversation—see what’s next in digital manufacturing! Celebal Technologies The Operational Technology (OT) Data Liberator by Celebal Technologies extracts, processes, and integrates OT data into a centralized Lakehouse, ensuring metadata synchronization, real-time streaming, historical data retrieval, and a resilient data pipeline—all while maintaining full data governance and simplified infrastructure management within the customer’s network. Deployed as a Kubernetes workload at the edge, the Liberator streams MQTT data directly into Azure IoT Operations and can be configured to leverage Akri-enabled connectors for protocol translation, eliminating traditional data silos and accelerating digital transformation. Powered by Azure IoT Operations, the OT Data Liberator delivers secure, scalable connectivity across legacy and modern OT systems, enabling data transformation and management. From manufacturing and energy to utilities and resources, this collaboration empowers industries to optimize operations, enhance security, and scale digital transformation with confidence. Learn more here. Litmus Litmus, a leader in Industrial Data Operations, has partnered with Microsoft to accelerate industrial transformation by integrating Litmus Edge with Azure IoT Operations. This collaboration enables seamless connectivity through the Akri Litmus connector, supporting data processing and management of factory edge devices while bridging legacy OT systems with Microsoft’s edge to cloud technologies, including Azure Arc and Microsoft Fabric. The joint solution delivers zero-code protocol integration, centralized device orchestration, and real-time insights, simplifying edge-to-cloud data operations. Key outcomes include faster AI deployment, reduced downtime, improved product quality, and enhanced operational agility across industrial environments. Together, Litmus and Microsoft offer a unified scalable platform that empowers manufacturers to modernize operations and easily replicate lines and sites to unlock the full potential of their industrial data. Visit the Microsoft booth to see a live demo of this powerful edge-to-cloud solution in action and learn more here. Loopr.ai Loopr delivers real-time, AI-driven visual inspection for complex assemblies, performing over 400,000 inspections annually to enhance quality consistency, workforce efficiency, and cost reduction. With Azure IoT Operations, Loopr efficiently integrates with on-premise factory systems, enterprise ERP, and cloud analytics like Microsoft Fabric, enabling manufacturers to deploy AI-driven quality control within their existing Azure infrastructure. Loopr powered by Azure IoT Operations enables customers to overcome scaling challenges, optimize workflows and streamline edge-to-cloud data transport, enabling real-time analytics and enterprise-wide deployment. For example, a North American automotive manufacturer recently integrated Loopr's AI-powered visual inspection system to automate their final quality checks. This implementation led to improved precision on the production line and a reduction in defect rates. MTEK MTEK Industry AB is transforming digitalization of discrete manufacturing with its Digital Production System and advanced integration platforms. Through collaboration with Microsoft, MTEK has successfully deployed MBrain and the Manufacturing Integration Platform (Mint) in production facilities. Utilizing the full Microsoft stack, including Azure IoT Operations, Dynamics 365, Microsoft Fabric and Teams (to name a few), MTEK achieves IT/OT/human convergence, optimizing operations while reducing environmental impact. MBrain integrates into Azure IoT Operations supporting MQTT and OPC UA, enabling immediate data monitoring and management. Together, Microsoft and MTEK deliver easily integrated data exchange between edge devices and the cloud by supporting real-time analytics and decision-making. Join us at Hannover Messe 2025 to discover how MBrain's real-time data analytics and IT/OT/human convergence empower manufacturers to achieve total value capture. Schneider Electric Schneider Electric enables digital transformation by integrating world-leading automation and energy technologies, endpoint to cloud connecting products, controls, software and services, across the entire lifecycle, enabling integrated company management, for homes, buildings, data centers, infrastructure, and industries. Schneider Electric is partnering with Microsoft to transform manufacturing into an AI-powered, open, software-defined industry. Microsoft's AI, Edge & Cloud patterns are combined with Schneider Electric's advanced, secure, and user-friendly industrial automation edge solution. Join us at HMI 2025 to experience this direct-to-cloud, secure interface that empowers innovative, data-driven approaches to modernize processes and products using AI agents and digital twin solutions with real-time simulation. Siemens Siemens develops technologies that power progress across industrial automation, infrastructure, transportation, and healthcare, with a strong emphasis on digital solutions and sustainability globally. The collaboration with Siemens leverages Siemens Industrial Edge and Microsoft Azure IoT Operations to create integrated, data-driven production environments that address customer pain points. This partnership helps ensure data flow from the shop floor to the cloud, empowering manufacturers to harness advanced technologies like AI and digital twins to streamline their production processes. Learn more about how Siemens and Microsoft are partnering to accelerate IT and OT integration at HMI 2025. Sight Machine Sight Machine’s industrial AI data platform, now deployable at the edge with Azure IoT Operations, unifies real-time production data, enhancing data accessibility and productivity. At the Microsoft booth come and discover how Sight Machine and Microsoft are revolutionizing beverage bottling operations by reducing downtime and increasing availability through real-time plant data, AI-driven insights, and collaboration tools, all powered by Microsoft’s secure, scalable cloud infrastructure. Microsoft fosters collaborative innovation, empowering partners to drive industrial transformation. At Hannover Messe 2025, Sight Machine will also demonstrate its integration with NVIDIA Omniverse, offering real-time 3D visualization, rapid troubleshooting, and root cause analysis. Co-developed by Microsoft, NVIDIA, and Sight Machine, this solution enhances manufacturing performance. Visit the NVIDIA booth to learn more. Symphony AI SymphonyAI revolutionizes the Intelligent Factory with Predictive, Generative and Agentic AI solutions for industrial verticals across manufacturing, consumer goods and energy. Their software drives end-to-end digital transformation from edge to cloud, integrating data sources, contextualizing information, and powering AI-driven applications. At HMI, discover how SymphonyAI’s IRIS Foundry Industrial DataOps platform is extending capabilities to the edge to help manufacturers leverage factory data to expedite AI drive value in maintenance, quality, process optimization, closed-loop operations, and overall plant performance. The new edge capabilities easily and securely connect to factory systems, store and transform data, automate workflows and leverage Azure IoT Operations Dataflows and MQTT Broker to smoothly transport data to IRIS Foundry, unlocking actionable AI for factory operations. Don't miss this opportunity to see how we can transform your operations—join us at HMI for the demo. Learn more AI and the adaptive cloud approach are transforming how industries design, build and operate, driving the next wave of efficiency, agility, and innovation. To fully harness this potential, organizations should embrace a collaborative ecosystem that fosters AI-driven insights, simplified data integration, and secure digital transformation. The future of manufacturing is intelligent, interconnected, and AI-powered—and success depends on a strong partner network, a flexible cloud strategy, and a commitment to open, multi-cloud innovation. By working together, we can accelerate industrial transformation, overcome complex challenges, and unlock the full power of smart manufacturing. Learn more about the adaptive cloud approach and explore comprehensive cloud-to-edge scenarios designed for specific industry needs with Arc Jumpstart Agora.Enable an Industrial Dataspace on Azure
What is an Industrial Dataspace? An industrial dataspace is an environment designed to enable the secure and efficient exchange of data between different organizations within an industrial ecosystem. Developed by the International Data Spaces Association, it focuses on key principles such as data sovereignty, interoperability, and collaboration. These principles are crucial in the context of Industry 4.0 where interconnected systems and data-driven decision-making optimize industrial processes and create resilient supply chains. A tutorial with step-by-step instructions on how to enable an industrial dataspace on Azure is available here. Use Case: Providing a Carbon Footprint for Produced Products One of the most popular use cases for industrial dataspaces is providing the Product Carbon Footprint (PCF), an increasingly important requirement in customers' buying decisions. The Greenhouse Gas Protocol is a common method for calculating the PCF, splitting the task into scope 1, scope 2, and scope 3 emissions. This example solution focuses on calculating scope 2 emissions from simulated production lines using energy consumption data to determine the carbon footprint for each product. Accessing the Reference Implementation The Product Carbon Footprint reference implementation can be accessed here and deployed to Azure with a single click. During the installation workflow, all the required components are deployed to Azure. This reference implementation supports data modelling with IEC standard Open Platform Communication Unified Architecture (OPC UA), aligned with the OPC Foundation Cloud Initiative. It also uses the IEC standard Asset Administration Shell (AAS) to provide product semantics, creating a Product Carbon Footprint AAS for simulated products and storing it in an AAS Repository. Finally, the implementation uses the IEC/ISO standard Eclipse Dataspace Components (EDC) to establish the trust relationship between the manufacturer and the customer, enabling the actual PCF data transfer via an OpenAPI-compatible REST interface. Conclusion Enabling an industrial dataspace on Azure can help manufacturers meet regulatory requirements, optimize industrial processes, and improve customer engagement by leveraging modern cloud technologies and standards to provide a secure and efficient data exchange environment, ultimately driving transparency and sustainability in the manufacturing industry.Partners accelerating industrial transformation with Azure IoT Operations
In the digital age, the essence of innovation lies not only in groundbreaking technology but also in the power of collaboration. At Microsoft, we have always recognized that our success is intertwined with the success of our partners. Our platform products, including the newly released Azure IoT Operations, are designed to be the foundation upon which our partners can build transformative solutions. These collaborations are more than just business arrangements; they are the bedrock of a thriving ecosystem that drives innovation, addresses customer needs, and propels industry standards forward. Partnerships enable us to extend our reach and impact far beyond what we could achieve alone. By combining our technological prowess with the domain expertise and creativity of our partners, we create a dynamic synergy that fosters groundbreaking advancements. This collaborative spirit is vital as we navigate the complexities of the Internet of Things (IoT) landscape, where diverse applications and specialized knowledge are paramount. Our partners bring unique perspectives and capabilities to the table, ensuring that Azure IoT Operations can cater to a broad spectrum of industries and use cases.
