artificial intelligence

13 Topics
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industrial transformation with a robust partner ecosystem","conversation":{"__ref":"Conversation:conversation:4396062"},"id":"message:4396062","revisionNum":8,"uid":4396062,"depth":0,"board":{"__ref":"Blog:board:IoTBlog"},"author":{"__ref":"User:user:985860"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":"","introduction":"","metrics":{"__typename":"MessageMetrics","views":718},"postTime":"2025-03-25T11:57:44.845-07:00","lastPublishTime":"2025-03-25T18:06:27.037-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" 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. \n Challenges in meeting the promise of IT and OT integration \n 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. \n How an adaptive cloud approach supports operational transformation \n 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. \n 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. \n \n 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. \n Why a partner ecosystem is critical for enabling customer success \n 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. \n 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. \n 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. \n Learn more about how Microsoft, along with partners, is reimagining how intelligent digital threads and AI agents will transform the manufacturing industry here. \n Join Us - Industrial AI in Action at Hannover Messe 2025 \n 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. \n 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. \n Avanade  \n 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. \n Capgemini \n 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. \n 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! \n Celebal Technologies \n 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. \n 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. \n Litmus \n 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. \n Loopr.ai  \n 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. \n MTEK  \n 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. \n Schneider Electric \n 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. \n 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.  \n 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. \n Sight Machine \n 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. \n 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. \n Symphony AI \n 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. \n Learn more \n 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. 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","introduction":"","metrics":{"__typename":"MessageMetrics","views":7079},"postTime":"2023-02-02T10:00:00.048-08:00","lastPublishTime":"2023-02-02T16:40:32.111-08:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Industrial and commercial energy users are becoming increasingly aware of the importance of managing their energy usage due to multiple factors, such as the energy transition and increased price of gas and electricity, as well as the introduction of zero-emissions targets to address climate change. The increased importance of energy awareness and effective energy management is driving an evolution in energy management, where energy consumers are installing renewable energy generation systems and energy storage (batteries) behind their energy meter. Consumers are self-generating energy to minimize grid energy usage, and export surplus green energy to the grid to earn revenue, which provides the energy user with multiple levers on both energy consumption and energy generation to better influence their net energy expenditure. \n   \n The evolution in energy usage behavior is occurring in across the market from retail to industrial. In the retail energy consumer space, smart appliances, batteries, and EV charging are transforming how energy is used at home. At the industrial level, IoT sensors and intelligent control systems are changing the energy usage profile of buildings and the industrial processes to drive energy efficiency, emissions reductions, and energy cost outcomes. \n   \n Demand-side energy management not only benefits customers in lowering their energy costs and emissions but for power system operators, it forms the other side of the equation to solve the energy transition, with renewable energy generation and energy storage on the supply side. Power system operators encourage large customers to flex their demand as the grid requires - either curtailing load when energy demand is high and renewable generation is low or increasing demand when energy demand is low and renewable energy is abundant. Both actions assist in keeping the grid stable during these times, and electricity tariffs and other ancillary markets exist to incentivize and reward energy users for doing so. \n   \n IoT and analytics are enabling the energy transition \n   \n IoT, data management and analytics platforms enable customers, both large and small, to enhance their energy management and optimize their energy usage from planning, through to real-time operational decision-making. \n \n Access to real-time energy consumption through smart meters and cost-effective sub-metering \n Easy to configure platforms for data management, analysis, reporting and visualization, \n Energy analytics, optimization and control solutions for coordination and optimization of energy generation and usage \n \n The application of IoT and analytics to demand management evolves in three levels of maturity: \n \n Monitoring and visualization \n Intelligent device control \n Advanced system control \n \n   \n Energy analytics reducing the cost of water supply operations \n   \n SA Water is an excellent example of a water utility that has actively embraced a new way of managing its energy usage by combining demand-response and energy generation at scale. SA Water provides water and wastewater services to over 2.7 million customers in South Australia, Australia. It is a major energy consumer since water supply is primarily driven by electrical energy for pumping and treatment processes, and its combined facilities can consume more than 10% of the region’s electricity load. \n   \n SA Water’s innovative energy strategy is based on proactive management of energy usage that leverages key features: \n   \n \n Flexible electricity demand. Water supply systems incorporate water storage at multiple points between the source and the customer tap, which can be alternatively seen as energy storage, and provides significant demand flexibility. \n A diverse portfolio of behind-the-meter energy generation. SA Water committed to investing in renewable energy generation at scale, with a total of 154 MW of solar PV energy generation capacity and 17 MW (34 MWh) energy storage has been installed across 33 operational sites, which adds to an existing portfolio that includes nearly 10 MW biogas energy generation and 3MW of hydro generation (energy-recovery). \n Participation in the wholesale electricity market. Sourcing electricity from the wholesale market provides access to lower-cost energy throughout the day, provided that the consumer can operationally flex its load to minimize its net electricity consumption during extreme price periods. \n \n Enabling this innovative approach required developing an intelligent energy management system to maximize the benefit of energy generation systems operated in harmony with demand-side management of water supply systems.  SA Water has applied an intelligent energy management system to schedule major water supply pipeline systems, which consume significant amounts of energy each year to supply its customers with drinking water. \n   \n What makes a real-time intelligent energy management system? \n   \n Determining decisions in real-time that optimally balance conflicting objectives of energy cost reduction, water security and customer service delivery required the implementation of four main components: \n   \n \n A reliable energy data management platform to wrangle real-time data from multiple sources, including IoT, SCADA, market APIs and weather services, and supporting contextual data from enterprise asset management and other business systems, \n An AI platform for developing and deploying predictive models to forecast weather, price and water demand, and any other dynamic inputs into energy decisions, \n A fast energy analytics platform capable of processing the data, simulating, and optimizing operations over a forward-looking horizon and returning clear and actionable information to system operators, and \n An application or user interface that allows system operators to effortlessly prepare and trigger analyses and provides actionable information in a concise and easy-to-understand interface, which enables energy operations planning to be carried out in an efficient and timely manner. \n \n \n The data management layer structures incoming data in a discoverable and accessible database. The database co-mingles data from multiple sources, including market databases, industrial SCADA and PLCs at pumping stations and treatment plants, and diverse IoT data streams from energy meters, weather sensors and telemetry at small-scale solar sites. Additional asset and market data relevant to energy management are referenced from the enterprise asset management (EAM) system and geographical information system (GIS), and market data sourced from the market management system (MMS) database provided to wholesale energy market participants. The energy analytics component is the beating heart of the energy management system and drives the simulation, and optimization of system operations around energy price, water demand, and intermittent energy generation. The simulation and optimization platform is coupled with an artificial intelligence (AI) demand forecasting platform that uses machine learning models trained on historical data to predict future profiles for price, solar generation, and water demand over a multi-day horizon.  The real-time connection between data, forecasting and the simulation model creates a digital twin, which gives system operators the capability to optimize short-term operations on demand, or optimally schedule outages to minimize energy risk. \n   \n The scheduling application is a simple UI and optimization orchestration layer. The application allows system operators to review and override key operational inputs, initiate scheduling runs, and review optimized schedules. And a back-end configuration interface allowed modelers to manage optimization model versions and to configure input data mapping from the data layer to the optimization model. \n   \n \n   \n In SA Water’s case, the scheduling system was initially applied to four major water transfer pipelines, which collectively pump up over a gigalitre of source water per day, over distances of more than 60 km, to supply half of the annual water supply volume in South Australia. Each transfer pipeline comprises 2-3 pump stations in stages, with intermediate balancing storage, and terminal storage in open reservoirs that supply the metropolitan water network. The pump stations consume up to 80MW of electricity and represent approximately half of the energy demand in SA Water’s energy portfolio. \n   \n An ideal operational schedule needs to co-optimize short-term energy decisions considering multi-day production targets, asset & equipment outages, water storage and water supply constraints and objectives. This is a highly constrained optimization, which needs to be solved efficiently to meet operational decision-making timeframes. \n   \n The PLEXOS simulation and optimization platform enabled water system modellers to define a techno-economic model of the water system, which can comprise water treatment plants, transmission pipelines, pumping stations and storages. Behind-the-meter energy generation and battery energy storage systems (BESS) were also modelled, as well as the financial components of energy and reserve markets. This allowed the complete specification of a model for each operational facility, according to its unique water system topology, as well as the combination of power generation behind-the-meter, which provided a solution that is extensible to different water systems. \n   \n The optimization platform supported integration with the scheduling application through an API for remote execution of models on-premise or scalable computing on cloud infrastructure. The scalability and flexibility of the platform ensured that the performance requirements for real-time decision-making could be met for a range of model complexity. \n   \n A flexible energy management platform provides a one-size-fits-all solution \n   \n The energy management approach also extends to the operation of wastewater treatment facilities, which produce methane-rich biogas. The utilisation of biogas to supply power and heat to the wastewater plant is a long-held practice in the water industry. \n The ability to export surplus energy to the wholesale electricity market, plus the additional solar PV and battery energy storage system (BESS) behind-the-meter, makes these systems' operation more complex. A real-time optimization was needed to optimize the energy generation from multiple sources while satisfying multiple process constraints in the wastewater treatment process. \n   \n Using the same approach as applied for water pumping operations, a model was developed that co-optimizes the dispatch of the gas engines around the solar PV, BESS and operational load to minimize the net electricity cost, and minimising the amount of surplus biogas flared, while ensuring sufficient process heat is delivered to the process and connection point export limits are satisfied. \n   \n Applying a single energy management platform to these two distinct operational systems highlights the flexibility of the approach. The key is the flexibility of the simulation and optimization engine to universally model any industrial process system, creating the potential for many businesses to apply this approach to diverse operational facilities across different sectors. \n   \n \n   \n Robust energy portfolio management is underpinned by analysis \n   \n In addition to real-time analysis and decision-making, the energy analytics platform provides a tool for energy portfolio management requirements, including: \n \n medium-term operational planning, \n long-term asset investment analysis, \n energy portfolio price risk modelling, and \n electricity cost forecasting and budgeting. \n \n This modelling helps to plan investment in equipment and tune real-time operations, as well as monitor the performance of the energy strategy as part of a complete business energy management solution. \n   \n Interested to make better energy decisions? \n   \n Demand management is increasingly important for energy consumers and is also becoming more complex at the same time. Energy managers require an integrated digital solution that combines IoT data acquisition, data management and energy analytics to determine operations that holistically balance operational requirements and energy performance.  The approach to demand management can be tailored to the capability and maturity of an organisation, from energy monitoring to energy portfolio management. \n   \n SA Water’s example shows how intelligent energy management can be practically applied for complex operational systems that are coupled with distributed energy resources. This example has a broad range and application to other energy-intensive industrial and commercial sectors, including mining, oil & gas, manufacturing, or datacenter operations, which will increasingly seek to better manage their energy consumption and compliment their usage with self-generation to maximum effect. \n   \n Energy Exemplar is committed to working closely with industrial energy consumers to enable the evolution of their energy management approach by applying a flexible, high-performance energy analytics platform. PLEXOS and PLEXOS Cloud are eliminating the technological barrier to implementation of demand management and advanced energy portfolio management, which will assist large electricity consumers in managing energy costs and assisting with the overall energy transition. \n   \n References \n   \n May, R., 2021, Maximising the benefit of renewables with an intelligent management system, https://wattclarity.com.au/articles/2021/01/maximising-the-benefit-of-renewables-with-an-intelligent-energy-management-system/ \n   \n Anderson, H., May, R. and Duncker, A., 2023, Optimising a Zero Cost Energy Future for the Water industry, in proceedings of OzWater AWA National Conference, May 2023 \n   ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"14111","kudosSumWeight":2,"repliesCount":2,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzMxNjM3LTQzNzk0NGk1NDU3NjVGQjcyNzAxNzND?revision=9\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDI","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzMxNjM3LTQzNzk0M2lFNTMyQjlCM0I3QjM1MzND?revision=9\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDM","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzMxNjM3LTQzNzk0NGk1NDU3NjVGQjcyNzAxNzND?revision=9\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDQ","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzMxNjM3LTQzNzk0NWlBQzc0NkNGRjgwMUQ2MEND?revision=9\"}"}}],"totalCount":4,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":null,"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:3765162":{"__typename":"Conversation","id":"conversation:3765162","topic":{"__typename":"BlogTopicMessage","uid":3765162},"lastPostingActivityTime":"2023-03-14T02:18:12.673-07:00","solved":false},"User:user:290209":{"__typename":"User","uid":290209,"login":"jryfar","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/dS0yOTAyMDktNDEyMzM0aTM5MTg1MEFCMjlCRTk4Mzk"},"id":"user:290209"},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzY1MTYyLTQ0OTYwMGlFNEM0NjBGQjA4NjZENDkw?revision=3\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzY1MTYyLTQ0OTYwMGlFNEM0NjBGQjA4NjZENDkw?revision=3","title":"CLO20_Port_005Small.jpg","associationType":"TEASER","width":4666,"height":3111,"altText":null},"BlogTopicMessage:message:3765162":{"__typename":"BlogTopicMessage","subject":"Bring all your workloads to the edge with AKS Edge Essentials. Now Generally Available!","conversation":{"__ref":"Conversation:conversation:3765162"},"id":"message:3765162","revisionNum":3,"uid":3765162,"depth":0,"board":{"__ref":"Blog:board:IoTBlog"},"author":{"__ref":"User:user:290209"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" \n Announcing the General Availability (GA) of Azure Kubernetes Service (AKS) Edge Essentials! AKS Edge Essentials has been developed as a managed Kubernetes for the operational edge on small-footprint devices to orchestrated workloads and drive business optimization. ","introduction":"","metrics":{"__typename":"MessageMetrics","views":17562},"postTime":"2023-03-13T23:59:00.032-07:00","lastPublishTime":"2023-03-13T23:59:00.032-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Today, at Embedded World, we announce the General Availability (GA) of Azure Kubernetes Service (AKS) Edge Essentials. AKS Edge Essentials has been developed in response to customer needs for Kubernetes at the operational edge on small-footprint devices for orchestrating workloads and driving business optimization. During the Preview period, we iterated with early adopters to release new features and updates. Our GA milestone signifies that we are now ready to engage with any business ready to go through their own Operational Technology (OT) transformation. \n   \n We are delighted to have reference implementations and samples available from our launch partners Scalers.ai, Anicca Data and Arrow, ready-to-use on edge devices from our hardware partners – Lenovo and Intel. These solutions make it easy for customers to get started and build secure, durable, and scalable solutions at the edge. \n   \n \n   \n Our Journey \n The genesis of our product was driven by customer insights.  Customers are building the next generation edge applications with modern cloud tools and platforms like containers, HTML5, WASM, and various AI runtimes to enable continuous integration, which ultimately leads to more efficient software development and faster adaptations to changing business dynamics. Customers plan to orchestrate containerized microservices, functions, and data spanning the cloud and OT infrastructure. Kubernetes, with its proven reliability on cloud workloads and broad ecosystem support, has become the de-facto platform for running applications; –it is the new OS powering cloud native applications whether they run in the cloud or on premises. \n   \n OT deployments on the edge are evolving from single machine setups that rely on the cloud for intelligence to multi-node configurations that need to make collaborative, near real-time decisions on the edge.  These are devices which sense things (cameras, medical sensors), drive physical state changes (robots, industrial machinery), or operate as single-purpose appliances (industrial gateways, Point-of-Sale machines, digital signs).  With an on-premises Kubernetes solution, customers can manage both Linux and Windows workloads on these devices which interact directly with the physical world. \n   \n AKS Edge Essentials is a Microsoft managed lightweight Kubernetes distribution designed to easily deploy Kubernetes on OT edge devices. Microsoft supports the entire software stack from kernel to cloud, Windows to Azure so that customers can focus on creating their unique business value. \n Our Value Proposition \n While AKS Edge Essentials utilizes a standard open-source Kubernetes distribution, it offers unique value tailored for building, deploying, and operating OT solutions: \n \n Small footprint with a simplified deployment experience – At the operational edge, containerized workloads generally must run alongside other applications like a Point-of-Sale application or a healthcare application on an MRI machine. AKS Edge Essentials consumes limited resources. In its minimum configuration, AKS EE can be installed on any Windows device with 2.5GB of free memory.  In addition to this, AKS Edge Essentials enables users to specify the resource configurations up-front so that they can divvy up resources on the host machine between containerized workloads and native application.  An AKS Edge Essentials cluster can be deployed on a single machine or in a full-fledged Kubernetes fashion – across multiple machines depending on customer scenarios. \n Fully managed stack – Microsoft supports and manages the entire stack - from hardware drivers to cloud services and everything in between. Customers can choose the 10-year Long-Term Servicing Channel (LTSC) version of the Windows IoT OS, ensuring long-term stability with critical and security fixes. The evergreen CBL-Mariner Linux VM for the Kubernetes control node is supported and patched for all known CVEs with an A/B style update to both the VM image and the Kubernetes platform.  This ensures that the application stack is in lock-step with the cloud and open-source K8S distribution, enabling workload portability.   Container workloads and cluster configurations can be managed and secured using GitOps and Defender, and your infrastructure can be managed using Intune or Azure Arc. \n Cloud-based management using Azure Arc - Deployments to the lighter end of the OT edge are characterized by 1000s of individual clusters, each running a few containers/workloads in contrast to the cloud in which you have fewer clusters with 1000s of containers running in them. The distributed nature of clusters at the OT edge can quickly become a management challenge and that is where Azure Arc comes in. AKS Edge Essentials clusters can be Arc-enabled and managed as an Azure resource from the Azure portal. This enables customers to streamline deployment and management using cloud services like GitOps, Azure monitor, Azure Policy and other Arc-enabled services that can be extended to you AKS Edge Essentials cluster. \n \n   \n Today over 100M Windows devices are used in embedded scenarios world-wide. AKS Edge Essentials is available on multiple Windows SKUs including Windows IoT Enterprise, Windows Enterprise, Pro and Server. Many customers prefer Windows IoT Enterprise due to its focus on OT scenarios and experiences like advanced lockdown features, kiosk and dual operator modes, device image optimization capabilities and a 10-year LTSC.  And, of course, it offers the same world-class enterprise manageability and security capabilities that Windows is known for.  You can Azure-Arc enable your Windows IoT devices and tap into Arc-enabled Azure services. The benefits of Windows IoT, coupled with the Linux-based Kubernetes at the edge with AKS Edge Essentials, helps customers truly enable OT transformation at the edge. \n   \n The Art-of-the-possible at the OT Edge \n Customers are embarking on OT transformations to increase efficiency, quality, and sustainability. Let’s take the example of a fast-food chain restaurant. \n To optimize your operations, let’s say you use a ML model to predict the quantity of French fries that need to be cooked at any point in time. Since you have a chain of 4000 stores across the country, you create your model in the cloud using data from across all stores. The prediction of the model is not always accurate as the sales at a given location could fluctuate based on local events like a concert, a game, or the weather. If you could collect data from the point-of-sale systems and the camera sensors in store and order requests coming in online, the forecast can be corrected to reflect a more realistic value and help optimize operations at the franchisee level. With AKS Edge Essentials you could have your microservices running in a cluster spanning across your existing infrastructure running your PoS application. Using Azure Arc-enabled ML services, you could deploy models trained in the cloud across all the stores. Data from the sensors in store can be processed in your cluster to further tune the model and publish events to the restaurant automation systems. This pattern can be extended to other OT devices like programmable logic controllers (PLCs), medical devices and other industrial equipment, to maintain peak operational efficiency and minimize downtimes. \n Our Go-To-Market Partners \n We are focused on energizing and expanding our partner ecosystem to create durable and scalable solutions across different use-cases and industries to help customers to quickly get started. \n Lenovo AKS Edge Essentials will be enabled across the ThinkEdge portfolio, including the newest member of the ThinkEdge family, the ThinkEdge SE10 with Intel® Atom™ processor . Lenovo’s ThinkEdge line of products enables customers to deploy solutions across the full spectrum of edge use cases. These devices are powered by Windows IoT with applications managed by AKS Edge Essentials. The SE10 adds to that line with a cost optimized yet powerful offering which is purpose built for OT edge workloads.​ \n   \n Scalers AI To demonstrate the opportunities, Scalers AI has developed a ready-to-scale solution that makes it easy to deploy and manage custom Linux and Windows based containers using a lightweight Kubernetes distribution on Windows IoT based Intel powered Lenovo ThinkEdge SE30 and SE450 hardware. The solution includes use cases for intelligent digital signage, sensor-based cold-chain monitoring and stream analytics that are common across manufacturing, transportation, logistics and retail. \n   \n Arrow Arrow offers a complete portfolio of solutions across partners like Lenovo, Microsoft, and Scalers AI to deliver business transformation at the operational edge. This includes Windows and Azure services including AKS Essentials, the ThinkEdge portfolio, Industry Specific Software from Scalers AI, and edge sensors required to deliver the full solution in production. Check out the demo and request access to the repo here. \n   \n Anicca Data Businesses looking to optimize their operations and customer engagement with video data analytics can leverage newly launched Anicca Vision – a custom built solution available from Anicca Data Science Solutions. It is an end-to-end CV platform encompassing Anicca’s video analytics modules, Intel’s hardware, AKS Edge Essentials and additional Azure services that enables AI-use cases like order accuracy, store inventory analytics, and footfall analysis for pass-by traffic and customers. \n   \n Tata Consultancy Services (TCS) TCS is leading customers across industries to realize new business models, deliver exponential business value, seamless customer experiences and drive sustainability goals with their dedicated Azure IoT CoE for full stack platform expertise including Intelligent Edge, Analytics and Digital Engineering solutions. Their solutions are even more flexible, scalable and resilient, along with AKS Edge Essentials unlocking the power of Kubernetes to the edge. TCS Industry solutions and toolkits for manufacturing and retail use cases make it easier for customers to go from proof of concept to production quickly. \n Check out Microsoft’s booth at Embedded World to learn more and see these partners in action! \n   \n What’s next? \n In the next few months, we will update the product with new capabilities like commercial support for multi-machine deployment and Windows containers along with additional Azure Arc enabled services on AKS Edge Essentials clusters. \n We are excited to see how customers will employ AKS Edge Essentials in their unique scenarios to drive innovation that benefits their business. If you would like to discuss your use case or learn more about the product, get in touch with the product team at projecthaven@microsoft.com. Get started with AKS Edge Essentials today! ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"11129","kudosSumWeight":9,"repliesCount":1,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzY1MTYyLTQ0OTYwMGlFNEM0NjBGQjA4NjZENDkw?revision=3\"}"}}],"totalCount":1,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[{"__typename":"VideoEdge","cursor":"MHxodHRwczovL3lvdXR1LmJlL0RPYXpjYXFmNUVjfDB8MjU7MjV8fA","node":{"__typename":"AssociatedVideo","videoTag":{"__typename":"VideoTag","vid":"https://youtu.be/DOazcaqf5Ec","thumbnail":"https://i.ytimg.com/vi/DOazcaqf5Ec/hqdefault.jpg","uploading":false,"height":113,"width":200,"title":null},"videoAssociationType":"INLINE_BODY"}}],"totalCount":1,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":null,"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:3725276":{"__typename":"Conversation","id":"conversation:3725276","topic":{"__typename":"BlogTopicMessage","uid":3725276},"lastPostingActivityTime":"2023-02-04T09:06:49.983-08:00","solved":false},"User:user:1041011":{"__typename":"User","uid":1041011,"login":"penorouzi","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/dS0xMDQxMDExLTQxODM3N2lGMTlBQjlEOUU5Mjc2QTdB"},"id":"user:1041011"},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzI1Mjc2LTQzNzc2Nmk4REYyN0UwQTAzMTg2RTMy?revision=29\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzI1Mjc2LTQzNzc2Nmk4REYyN0UwQTAzMTg2RTMy?revision=29","title":"Logo whiteboard (1).png","associationType":"TEASER","width":3000,"height":1674,"altText":null},"BlogTopicMessage:message:3725276":{"__typename":"BlogTopicMessage","subject":"Introducing KAN: An OSS project for Creation and Management of Computer Vision Edge Al Applications","conversation":{"__ref":"Conversation:conversation:3725276"},"id":"message:3725276","revisionNum":29,"uid":3725276,"depth":0,"board":{"__ref":"Blog:board:IoTBlog"},"author":{"__ref":"User:user:1041011"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" \n Introducing KAN - a new open-source project for vision edge Al projects from dev to ops ","introduction":"","metrics":{"__typename":"MessageMetrics","views":8405},"postTime":"2023-02-02T08:00:00.028-08:00","lastPublishTime":"2023-02-02T08:00:00.028-08:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" To address challenges of building scalable computer vision AI solutions, we are releasing KubeAI Application Nucleus for edge (KAN - in Mandarin means “to watch”, “to see”) - a Kubernetes-native solution accelerator that enables you to easily develop, orchestrate, and operate computer vision Al applications for the edge with full control and flexibility. \n   \n Edge AI applications allow organizations to easily extract actionable insights from unstructured data streams right where the data is generated, enabling the creation of environmentally aware business solutions. Parking operators can improve parking lot utilization by analyzing vehicle patterns. Retailers can improve store operations, and customer satisfaction by continuously analyzing customer behavior in-store. However, as organizations increasingly rely on edge AI to process data closer to the source, developers and solution operators face the challenge of developing and operating scalable, distributed Al applications across heterogeneous and hybrid edge environments. \n   \n \n\n   \n End-to-End Development and Operation Environment \n   \n KAN reduces the complexity and radically simplifies the process of building AI solutions at scale. It does so by providing a single self-hosted place for both developing AI Applications (what we call AI Skills) and then deploying and operating such applications across all your edge environments. \n   \n \n\n   \n With KAN, you have your own no- to low-code portal experience as well as APIs that you can use to develop custom AI applications in a matter of minutes. Your custom-developed application can ingest camera and sensor data, use AI models and various other processing techniques to analyze unstructured data and then export your structured output to your desired location locally, to other environments, or the cloud, all this happening close to your data source. When building your applications with KAN, you can leverage pre-built models from our partner’s Model Zoo or create your custom ML models with Azure Custom Vision or bring your existing ML Models developed externally. Your custom-created AI application can run accelerated on x64 CPU, Nvidia dGPU, Nvidia Jetson, and Intel iGPU out of the box. \n   \n \n\n   \n KAN is designed with machine learning operations (MLOps) in mind, providing support for active learning, continuous training, and data gathering using your ML models running at the edge. It seamlessly integrates with standard technologies such as Dapr, MQTT, ONNX, Akri, etc. As a self-managed solution, you can host it on your Kubernetes clusters anywhere across on-prem, cloud, and multi-cloud environments. It natively supports Azure Edge and Al Services like Azure loT Hub, Azure IoT Edge, Azure Cognitive Services, Azure Storage, Azure Arc, etc. \n   \n Get Started Even with no Azure Subscription or IoT Devices \n   \n Don't wait, try KAN today and experience the ease of developing and operating edge Al applications. You don’t need any edge hardware, you can get started with a few commands even without an Azure Subscription. \n \n Check out our GitHub Repo to learn more and Get Started Guide to start your journey. \n Watch a quick guide to the KAN portal to learn more about our concepts and user experience. \n ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"3380","kudosSumWeight":3,"repliesCount":3,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzI1Mjc2LTQzNzc2Nmk4REYyN0UwQTAzMTg2RTMy?revision=29\"}"}}],"totalCount":1,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":null,"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:3698233":{"__typename":"Conversation","id":"conversation:3698233","topic":{"__typename":"BlogTopicMessage","uid":3698233},"lastPostingActivityTime":"2022-12-16T08:00:00.042-08:00","solved":false},"User:user:244380":{"__typename":"User","uid":244380,"login":"TonySampige","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/m_assets/avatars/default/avatar-8.svg?time=0"},"id":"user:244380"},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNjk4MjMzLTQyNjU1M2k2MkRGNzJBQTA4QUQ1QzBD?revision=9\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNjk4MjMzLTQyNjU1M2k2MkRGNzJBQTA4QUQ1QzBD?revision=9","title":"HLS19_azureHologram_001.jpg","associationType":"TEASER","width":4000,"height":2668,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNjk4MjMzLTQyNjQyNGk4MUJEQTIzQUE0MDYxMTEz?revision=9\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNjk4MjMzLTQyNjQyNGk4MUJEQTIzQUE0MDYxMTEz?revision=9","title":"azure_deepstream_accelerator_architecture","associationType":"BODY","width":905,"height":635,"altText":null},"BlogTopicMessage:message:3698233":{"__typename":"BlogTopicMessage","subject":"Bridging Cloud services and AI solutions at the Edge","conversation":{"__ref":"Conversation:conversation:3698233"},"id":"message:3698233","revisionNum":9,"uid":3698233,"depth":0,"board":{"__ref":"Blog:board:IoTBlog"},"author":{"__ref":"User:user:244380"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" New open-source project to help streamline edge and cloud AI in partnership with Neal Analytics and NVIDIA \n ","introduction":"","metrics":{"__typename":"MessageMetrics","views":6913},"postTime":"2022-12-16T08:00:00.042-08:00","lastPublishTime":"2022-12-16T08:00:00.042-08:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Microsoft is excited to announce the open-source release of Azure DeepStream Accelerator (ADA) in collaboration with Neal Analytics and NVIDIA. \n   \n Creating an integrated edge and cloud AI solution is difficult. To tackle this challenge Microsoft partnered with Neal Analytics and NVIDIA to build an open-source solution that bridges the gap between Cloud services and AI solutions deployed on the edge; enabling developers to easily build Edge AI solutions with native Azure Services integration. Our goal is to support 1) Developers utilizing existing Azure services to leverage the power of computer vision at the edge via DeepStream “and” 2) Enable the DeepStream developers to leverage the power of Azure Services on the cloud. \n   \n Through ADA we aim to provide developers with the ability to create NVIDIA DeepStream AI based solutions and integrate them with a multitude of Azure services such as Blob Storage and Monitor. The open-source project includes tools to ease the developer journey including a region of interest widget and supplementary developer tools that developers can leverage to build, manage, and deploy their AI solutions to NVIDIA’s AGX Orin edge devices and more. Additionally, ADA provides support for 30+ pre-built AI models out of the box box (Nvidia, ONNX, TF, Caffee, Pytorch, Triton models) and the ability to bring your own Model/Container for deployment to IoT edge devices. \n   \n \n   \n Azure Deepstream Accelerator features \n   \n \n \n \n Simplified development process \n \n Auto selection of AI model execution and inference provider: One of several execution providers, such as ORT, CUDA, and TENSORT, are automatically selected to simplify the development process. \n \n Customizing Region of Interest (ROI) to enable custom use cases \n \n Region of Interest (ROI) configuration widget: a web app widget, is included for customizing ROIs to enable event detection for any use case. \n \n Simplified configuration for pre/post processing \n Developers can add a Python-based model/parser using a configuration file, instead of hardcoding it into the pipeline. \n Offering a broad Pre-built AI model framework \n Support for many of the most common CV models in use today, for example NVIDIA TAO, ONNX, CAFFE, UFF (TensorFlow), and Triton. \n Bring Your Own Model \n \n Support for model/container customization, USB/RTSP camera and pre-recorded video stream(s), event-based video snippet storage in Azure Storage and Alerts, and AI model deployment via Azure IoT Module Twin update. \n \n Multiple trackers \n \n Support for Nvidia tracker and 1PLight tracker for tracking use cases. \n \n End-to-end Defect detection Manufacturing Use Case with DeepStream and TAO Toolkit \n \n An example tutorial that illustrates how to build your own custom model using Nvidia's TAO toolkit and to integrate the model with DeepStream. The example uses a MaskRCNN model provided in the TAO toolkit for performing transfer learning. \n \n \n \n \n To check out the full project visit GitHub  \n   \n Our team is partnering with Neal Analytics to support this project and we can’t wait to see what the community builds!  \n   \n If you are new to DeepStream and want to learn more about it, we recommend checking out Nvidia’s documentation as well DeepStream SDK | NVIDIA Developer.  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Machine Learning for Industrial IoT applications","conversation":{"__ref":"Conversation:conversation:3610800"},"id":"message:3610800","revisionNum":16,"uid":3610800,"depth":0,"board":{"__ref":"Blog:board:IoTBlog"},"author":{"__ref":"User:user:212324"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" \n Learn how to build machine learning solutions for Industrial IoT using deployment samples available on GitHub \n   ","introduction":"","metrics":{"__typename":"MessageMetrics","views":7587},"postTime":"2022-09-01T11:36:11.631-07:00","lastPublishTime":"2022-09-01T11:36:11.631-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Artificial Intelligence is delivering unprecedented efficiencies in manufacturing. From optimizing operations to streamlining the supply chain to improving how employees work. Techniques like Machine Learning (ML) are a major component in most manufacturing transformation projects today. \n Applying Machine Learning to almost any problem is getting easier every day, but it requires a disciplined approach to evaluate a use case and build repeatable processes to scale these techniques beyond a single use case. In this article we will discuss an approach on how to evaluate use cases faster and build this repeatable process. Operationalizing machine learning projects require continuous experimentation and multiple components, for this article we will focus on aspects to accelerate ML lifecycle for Industrial IoT projects. \n A typical machine learning journey consists of three high level phases, that are executed in loop for continuous experimentation. Each of these phases answers a certain set of questions that are critical to understanding whether machine learning is the right approach to solve specific business problems and if it is, how to go about building and managing machine learning models. Here are some example questions for each phase: \n   \n \n \n \n \n Explore \n \n \n Experiment \n \n \n Operate \n \n \n \n \n Problem Definition \n - What is the business case? \n - What is the business impact? \n Data Acquisition \n - Is the data confidential? \n - Is historical data available? \n - Is the data relevant? \n - Is the data enough? \n - Is there a versioned dataset to start exploration? \n Exploratory Data Analysis \n - Is the data normalized? \n - Is the data distribution normal? \n - Is there a strong positive or negative correlation between features and target? \n - Is there a need to engineer new features? \n - Is the data collected at the right frequency? \n - Is there a versioned and labeled dataset ready for modeling? \n \n \n Business Alignment \n - Is the model objective aligned with business objective? \n Model & Data Management \n - Is the labeled dataset split evenly between train, test, and validation datasets? \n - Is the baseline modeling done with different algorithm(s)? \n - Are the model experiments tracked? \n - Are the model(s) registered? \n Training Pipeline \n - Is the model training automated? \n - Is the model training parameterized for multi-model retraining? \n   \n   \n   \n   \n   \n \n \n Business Actions Mapping \n - Does the business action require real-time prediction? \n - Does business action require prediction on the edge? \n Deployment & Monitoring \n - Is the model’s performance monitored? \n - Are the predicted results logged, to compare against actuals? \n - Is the model(s) deployment automated? \n   \n   \n   \n   \n   \n   \n   \n \n \n \n \n   \n Now let’s see some techniques to answer these questions and start building a machine learning solution with an example business case. All the sample code is available in our industrial-iot-patterns GitHub repo, along with many other samples to help implement IIoT and AI scenarios in Azure. \n   \n Explore – Problem Definition \n   \n Business Case \n \n The average scrap rate per shift is around (x)% even with measures to improve yield. \n Reducing scrap and understanding what variables are contributing to scrap is a critical business goal. \n Manufacturing lines are connected to PLC(s) that can provide variable data. \n Product quality check takes around 24hrs. and quality results are logged in a different system. \n \n Business Impact \n \n (x)% reduction in scrap == $(y) in savings \n \n High Level Potential Solution \n \n Build a model to predict quality failures in minutes. \n Help understand what parameters are contributing to the quality failures. \n Recommend actions to reduce quality failures. \n Recommend additional parameters or data collection methods to look at. \n \n Sample Design: \n \n Machine Data\n \n Generated via an IoT Edge Module \n Contains 40+ different sensor values \n Contains production batch number \n \n \n Production Quality Data\n \n Contains production batch number \n Contains quality error code for each batch \n 1 = Meets quality expectations | 0 = Does not meet quality expectations. \n \n \n \n  To generate the data, you can either follow the given prerequisites or add the SimulatedSensors module in your IoT Edge device. Here is the high-level design: \n   \n \n   \n Explore - Data Acquisition \n   \n For IIoT projects we often need to merge the near real-time telemetry data with data from other systems like MES, ERP Quality Management, etc. to build the initial dataset for modeling. Typically, this step may require heavy compute and scalable post processing storage. Hence, we are using Azure ML managed compute to process both the telemetry data directly from Data Explorer and merge that with the Production Quality Data from Data Lake. \n Once you have the ML workspace and datastore created, run the 1_create_raw_dataset.ipynb notebook to see the merged dataset in ML Studio. The notebook uses kqlmagic extension to fetch the sensor data directly data explorer. \n   \n \n   \n \n   \n Explore - Exploratory Data Analysis \n   \n Exploratory Data Analysis (EDA) is the first step before we build any custom models using machine learning. This is a critical and often complex step wherein we normalize & clean the data, understand data distribution, outliers, correlations and assess the data for various hypothesis and experiments. For our scenario we will do: \n   \n \n \n Data distribution and Correlation Analysis: This helps us understand which sensors are strongly correlated to predict the quality output. Run the 2_exploratory_analysis_feature_selection.ipynb notebook. Below histograms display the data distribution for each of our sensor values. Ideally, we are looking for sensors closer to a normal distribution. \n \n \n   \n \n Below correlation heat map shows the positive or negative correlation among the sensor values. Ideally, we are looking to understand correlations between our sensors and the output that we are trying to predict, Quality. \n   \n \n   \n \n \n Frequency Analysis: This helps us understand optimal frequency value (in Hz) at which we should collect the sensor values. 1Hz equals to 1 sensor value per second, 10Hz equals to 10 sensor values per second and so on. Below line graphs show various frequency comparisons and ideally, we are looking for if there are strong reasons to increase or decrease our data capture rate for each of the sensors. Run the 2_frequency_analysis.ipynb notebook. \n \n \n   \n \n   \n \n \n Baseline Modeling: This helps us understand which types of algorithms are best suited for the given dataset. Run the 3_baseline_modeling-automl.ipynb and 3_baseline_modeling.ipynb notebooks. Automated ML can help identify algorithms that best fit our data, along with providing an interactive dashboard to understand model performance, top features and extract patterns from our data. \n \n \n   \n \n   \n \n   \n \n   \n Below table shows the list of all the algorithms that we used for our baseline modeling, along with the model evaluation metrics. For our use case we are optimizing for f1-score and recall. See the ML objectives section for more details on how to determine which metric is right for your use case \n   \n \n   \n Experiment \n   \n Continuous experimentation is key to building machine learning models with high accuracy of its predictions. This often requires periodic retraining of your model with new data. It is also important to log each of these retraining experiments to compare model improvement over time and track model versions. So, to perform these experiments we will build an Azure Machine Learning Pipeline. Azure Machine Learning pipeline is an independently executable workflow of a complete machine learning task. An Azure Machine Learning pipeline helps to standardize the best practices of producing a machine learning model, enables the team to execute at scale, and improves the model building efficiency. Follow steps from here to build the pipeline. \n The pipeline has two steps: \n \n \n build-datasets: Builds and registers train and test datasets. \n build-model: Builds and registers a new model based on the features provided as a dynamic parameter. \n \n \n \n   \n This endpoint can now be triggered manually or automatically from tools like Synapse Pipelines or Logic Apps. \n   \n \n   \n Since the endpoint uses a parameter to select the sensors for modeling, we can now build multiple sensor models using a single pipeline endpoint. \n   \n \n   \n Each pipeline run job logs all the outputs so it’s easier to track and debug. \n   \n \n   \n Operate \n   \n Once the model is trained and registered it’s time to deploy and test our models against live data, also known as inferencing. For our scenario we want quality predictions every few minutes as the production cycle takes some time. So, we built another pipeline to generate predictions. Follow steps from here \n   \n \n   \n It has only one step but multiple parameters to dynamically fetch live data from data explorer and generate the prediction output. \n   \n \n   \n We can then integrate the model prediction pipeline with Synapse Pipelines to automatically trigger on a schedule and store the output predictions in a Database. Follow steps from here. \n   \n \n   \n \n   \n We can use the stored prediction outputs to compare against the actuals and see how our model is improving over time or if it needs retraining. \n   \n \n   \n Conclusion \n   \n Machine Learning is a major component in most manufacturing transformation projects today. A key skill for a machine learning project is the ability to understand the data coming out of both the IT and OT systems. Most customers do have experts who understand their domains very well, but they lack the skills to apply these new machine learning techniques to better reason about the data. If these domain experts are enabled with the right set of tools and techniques, they can amplify their skills. I hope the above approach helps to sharpen these skills. \n To learn more see our Industrial IoT patterns doc and deployment samples. As always, feel free to share your thoughts and ask your questions in the comments! 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weekly round up week 3, 🐮, AI on the edge, JulIoT, and secure embedded development","conversation":{"__ref":"Conversation:conversation:3577917"},"id":"message:3577917","revisionNum":9,"uid":3577917,"depth":0,"board":{"__ref":"Blog:board:IoTBlog"},"author":{"__ref":"User:user:112040"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" As we end the third full week of JulyOT, here's a round up of the IoT content we've shared so far. \n ","introduction":"","metrics":{"__typename":"MessageMetrics","views":3026},"postTime":"2022-07-22T08:00:00.040-07:00","lastPublishTime":"2022-07-22T08:00:00.040-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" As we wrap up the third full week of #JulyOT 2022, here's a round up of all the IoT content we've shared so far. Check back here every week for a weekly roundup, or catch the new content every week day in July at JulyOT.dev. \n   \n We also shared more of our JulIoT content - JulyOT in Spanish! \n   \n JulIoT \n   \n #JulIoT is JulyOT in Spanish! We want to bring JulyOT to as many developers as possible, so watch out for more languages next year! \n   \n \n   \n For JulIoT this week we previewed a set of upcoming livestreams in Spanish showing how to use the Seeed Studio reTerminal with Azure and AI! \n   \n \n   \n Beginners, makers and students \n   \n For our beginners track, we ran lesson 4 of IoT for Beginners this week as a live stream. \n   \n \n   \n We also gave a sneak peek at an upcoming series showing how to use the Seeed Studio reTerminal with Azure and AI! This is the same series we are running for #JulIoT, but in English. \n   \n \n   \n For those wanting to create an IoT-based make, or prototype a new IoT solution, we shared an IoT device list for prototyping, helping makers to find the right hardware for their needs. \n   \n \n   \n Embedded IoT \n   \n This week continues our series on nanoFramework with a blog post on building nanoFramework and interoperability.. \n   \n \n   \n We also looked at how to accelerate your Azure Sphere development with DevX, a component of Azure RTOS. \n   \n \n   \n AI at the edge \n   \n For AI at the edge we have more Azure Percept content with a blog post on the unlimited possibilities of Azure Percept, showing some of the use cases from bootcamps and hackathons. \n   \n \n   \n We also preview 4 upcoming livestreams on building autonomous robots using Project Bonsai. \n   \n \n   \n Finally we had an AMA and a great discussion on using IoT and AI in dairy farming with Bryn Lewis from NZ. Catch the recording here: \n   \n \n   \n Learning and certifications \n \n   \n Want to become a subject matter in the IoT Domain? Compete the hundreds of other developers who have joined the JulyOT Microsoft Cloud Skills Challenge! \n   \n We also focus on certifications with a blog post on the AZ-220 Azure IoT developer speciality certification. \n   \n \n   \n   \n Join us next week! 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quality control in manufacturing using Azure Percept","conversation":{"__ref":"Conversation:conversation:3546794"},"id":"message:3546794","revisionNum":9,"uid":3546794,"depth":0,"board":{"__ref":"Blog:board:IoTBlog"},"author":{"__ref":"User:user:1433653"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" CANCOM developed a solution detecting loose screws and bolts using Azure Percept  \n ","introduction":"","metrics":{"__typename":"MessageMetrics","views":2684},"postTime":"2022-07-05T08:00:00.044-07:00","lastPublishTime":"2022-07-05T08:00:00.044-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" \n   \n In manufacturing as well as other industries, safety, like logistics, needs special attention. Not only can a lack of safety mechanisms result in downtime and costly maintenance, but also, even worse, in physical harm to the staff operating the facilities. One of these safety obstacles is caused by loose screws and bolts. Constant vibrations, corrosion, and mistakes made during the assembly are the main drivers for a declining preload force of screws. Considering the harsh environment present in manufacturing and logistics, it’s only a matter of time until the first screw connections will fail. \n   \n Therefore, CANCOM developed a solution detecting loose screws and bolts using Azure Percept, while participating in the Azure Percept Bootcamp. This blog post shows how the solution works using the example of a drainage channel. \n   \n Solution architecture \n   \n Our continuous quality control solution monitors the state of screwed assemblies using Azure Percept. The following is the architecture for the solution. \n   \n \n   \n Core components \n   \n The core components of the solution are: \n   \n \n \n \n \n Component \n \n \n Description \n \n \n \n \n \n \n Azure Percept \n \n \n Captures the data stream from the vision module of the Azure Percept Dev Kit and uses a pre-built AI model to detect the state of screwed assemblies. \n \n \n \n \n IoT Hub \n \n \n Handles the communication with the IoT Hub in the cloud. \n \n \n \n \n Cognitive Services \n \n \n Service for training the AI model. \n \n \n \n \n Azure Percept Studio \n \n \n Management portal for an easy development and management experience of the Azure Percept solution. \n \n \n \n \n IoT Hub \n \n \n Receives the data from the modules running on the edge and enables the management of all workloads on the edge. \n \n \n \n \n Stream Analytics \n \n \n Processes the raw data from the IoT Hub and stores it in the Azure SQL Database. \n \n \n \n \n Azure SQL Database \n \n \n Storage to persist the data collected. \n \n \n \n \n Power BI \n \n \n Visualizes the data from the Azure SQL Database. Note that it is also possible to either skip the database or add a second output from the Stream Analytics service directly to Power BI to achieve dashboards consuming stream data and delivering near real-time insights on the current state of the monitored facilities. \n \n \n \n \n   \n Solution Implementation \n   \n For simulation of a channel drainage, a demo setup as shown below was used. \n \n   \n   \n After setting up the Azure Percept Dev Kit, the other components can be deployed and configured. \n First, the database is created with a table of the desired telemetry data schema. Then, an IoT Hub with a dedicated consumer group for the Stream Analytics service is needed. After that, the IoT Hub can be configured as a stream input in the Stream Analytics service. The Stream Analytics service uses its query to format the raw data of the IoT Hub and saves it in the database configured as output. \n   \n Training the model \n   \n As previously mentioned, the training of the AI-model was done using Azure Cognitive Services. We created a custom vision model and gathered our first images using the Image capture functionality in Azure Percept Studio. \n We then proceeded to tag the images with these tags: \n   \n \n \n \n \n Tag \n \n \n Description \n \n \n \n \n bolt ok \n \n \n A fully tightened screw or bolt \n \n \n \n \n bolt loose \n \n \n A screw or bolt that came loose. Usually where the thread of the screw was visible \n \n \n \n \n bolt \n \n \n A screw that is not inside a socket \n \n \n \n \n empty screw hole \n \n \n An empty hole missing a screw or bolt \n \n \n \n \n drainage channel \n \n \n The whole part where the screws are installed. \n \n \n \n \n   \n \n A screenshot of tagging images in Azure Percept Studio. \n   \n After tagging only a few images, we started our first training iteration to take advantage of the AI-assisted object detection for tagging further images. Also, this way, we were seeing a steady progress of our model and how it performed. This is the result of our first iteration: \n \n A screenshot of the result of our first iteration. \n   \n We didn’t have a lot of data yet, so the results were far from what we were hoping for, but it was a start. We proceeded to push our first iteration to the Azure Percept Dev Kit to see our model in action. \n \n A picture of how tightened screws were detected as empty holes. \n   \n The picture shows how the tightened screws were detected as empty holes. However, the loose bolts on the top are not being detected at all. At this point, we decided to gather more training data and enhance our model. Additionally, we were getting rid of the “drainage channel” tag, focusing on the remaining tags. \n To further progress with the model development, we came up with the following development cycle to make increments and achieve better results.  \n   \n \n A picture of the development cycle to make increments and achieve better results. \n   \n After just five iterations, we had gained a lot more training data for the model. We were not only using images from Azure Percept, but also taking pictures with our cell phone cameras, modifying the lighting, changing the background, and inserting or removing the screws in various ways. In this way, we got different scenarios to prevent our model from being overfitted. The results were promising: \n   \n \n A screenshot of the result after 5 iterations. \n   \n There was a significant increase in loose bolt and empty hole detection, however there were still some issues left to be addressed. Some parts were still hardly detected at all, and we were missing some training data for loose bolts. Seven iterations later we were finally satisfied with the results. Everything we trained the model for was now detected with an average certainty of over 70%. Also, the lighting was less of an issue for detection. \n   \n \n   \n Final Thoughts \n   \n Azure Percept offers a quick and convenient way to bring AI based visual and audio solutions to the edge without the need to go into the math behind the models to be trained. This enables a much faster time to market. In just about one day, it is possible to build a solution like the one presented above from scratch. \n   \n Even though the solution is not yet ready for production, it is a solid starting point to integrate AI-based solutions in your IoT solution. With the results of Azure Percept being brought to the cloud, the possibilities for further enhancements are huge. The next step could be notifications about changes in a screw’s state to help avoid further damage to the affected assembly and therefore prevent additional costs and injuries. No matter if used for quality control of production or to monitor the facility, adding a notification system will make sure the information is delivered to the right people at the right time. Using services like Logic Apps can help with the integration of various communication channels, like email or even a Microsoft Teams channel for the facility management. \n   \n To learn more about Azure Percept, visit these resources:   \n \n Learn about Azure Percept    \n Learn about Azure Percept Technical Documentation  \n Learn more about our customer and partner stories  \n \n   \n Microsoft Build 2022 Key Sessions   \n \n Microsoft Build Into Focus: Preparing for the metaverse  \n Embrace digital transformation at the edge with Azure Percept  \n \n   \n GTC Spring 2022 Key Sessions   \n \n Session: Transforming AI and ML at the edge with Microsoft and NVIDIA   \n Embrace digital transformation at the edge with Azure Percept  \n \n   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Percept shines light on Holland Museum collections","conversation":{"__ref":"Conversation:conversation:3543219"},"id":"message:3543219","revisionNum":12,"uid":3543219,"depth":0,"board":{"__ref":"Blog:board:IoTBlog"},"author":{"__ref":"User:user:1064900"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" \n Azure Percept makes it possible to expose long hidden works of art ","introduction":"","metrics":{"__typename":"MessageMetrics","views":2768},"postTime":"2022-06-27T08:00:00.037-07:00","lastPublishTime":"2022-06-27T08:00:00.037-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" \n   \n The Holland Museum is a small, yet innovative museum located in Holland, on the western coast of Michigan. The museum owns and operates four buildings: the main galleries in an old Post Office building, the Settlers House, the Cappon House, and the Armory. The museum is proud to collect artifacts and stories from Holland’s early settlers, the original inhabitants of the area, and the latest wave of immigrants to the area. \n   \n I serve on the Holland Museum’s Board and wanted to use Azure Percept to help the museum showcase more of its artifacts. More than 100,000 artifacts sit in the Holland Museum basement, with only 700 artifacts on display and visible to the public. \n   \n In April 2022, the Museum was preparing for the Tulip Time festival in Holland, Michigan where 500,000 visitors arrive to see millions of tulips in bloom. The Museum’s goal was to engage Tulip Time visitors with Azure Percept and detect the “dwell time” visitors spent with each artifact. The results the museum staff and I experienced were amazing and will further shape the way future exhibits are designed and monitored. \n   \n In this blog post, I will be sharing the ways that the Holland Museum staff were able to accomplish their goals of sharing the museum's story and large collection of artifacts while also exploring how Azure Percept can bring some of the artifacts that were once out of sight, into the public's view. \n   \n Azure Percept and Holland Museum collections overview \n   \n A brief demo of the Azure Percept shines light on Holland Museum collections can be found in the following YouTube video: \n \n   \n Azure Percept and Holland Museum collections Implementation \n   \n Phase 1: Getting Started \n The first stage of the journey was training the museum staff about Azure Percept. I used two Azure Percept DKs; one unit was used to detect tulips, and the second was used to detect people in front of the display. \n   \n Azure Percept is very easy to use. One museum staff member attended the meeting virtually, so we shared our screen and logged in to Azure Percept Studio. She was able to remotely upload images of 10 tulip varieties, build the bounding boxes, and press the train button. Thirty minutes later, we had the new AI model running Tulip Detecting with Azure Percept. It was that easy! \n \n The following 3 key steps were followed in this phase: \n \n Step 1: Getting the DK set up. \n Step 2: Azure Percept Studio: Upload 15 images for each tulip species, drag out the bounding boxes, and select the respective tag from the menu. \n Step 3: Deploy the newly trained AI model to Azure Percept. \n \n   \n Phase 2: Training our own AI model \n A week later, I revisited the museum to review the model. The amazing part is the museum staff was able to train an intern to upload images and had also trained her on the AI model. This democratized Edge AI in action with Azure Percept, as the knowledge and processes were being organically transferred throughout the museum. At this second meeting, we walked through the iteration test images to better understand the issues in training images that were causing incorrect guesses. Our intern was then able to update the model and rebuild the bounding boxes prior to the influx of tourists that would arrive in West Michigan. \n   \n Visitor interaction \n Visitors walked up to the exhibit, quickly read through the instructions, pulled out their phones and swiped through the images of tulips they took during their Tulip Time visit. They held up their images to the camera and watched for tulip variety detection. Some visitors noted they had to hold the phone/images very close to the camera for detection to occur. Others noticed that images with many tulips triggered detection events as easily as images of a single flower. \n   \n Precision of the Model \n Initially only 15 images were uploaded for each species, but the museum staff noticed there was some misrecognition occurring. We learned that adding more images improved the initial model. The staff also learned that using images with close-ups and isolated images of tulips was much more effective than using images with a whole field of tulips. The staff revised the AI model and trained seven additional iterations before the model was ready for visitor use. \n   \n \n   \n Visualizing the data \n   \n The exhibit was a huge success as it introduced visitors to new technology, connected visitors with tulip varieties (which represent a rich Dutch heritage in the local area), and provided a fun and interactive way for visitors to engage with the Museum’s artifacts and exhibits. \n   \n The most exciting part of this exhibit was viewing the live data connection using PowerBI. We could see when people were standing in front of the exhibit. We could also see at night that traffic lights in the museum gallery were being detected when no people or cars were being detected. Car detection was interesting as well. When a horse drawn carriage with two rows of seating was in front of Azure Percept, it was detected as a “car.” \n   \n \n   \n Next steps: The future of Edge AI: Azure Percept in museums \n   \n It’s crazy to think about how far we have come in making Edge AI and customized AI models accessible and available for public use with Azure Percept. To get started, you really only need to get an Azure Percept Dev Kit, connect the kit with the self-contained out-of-box experience/user interface, set up and train an AI model with Azure Percept Studio and then deploy your model to the device. With Azure Percept, you get the added benefit of working on the Edge, where internet connection is not a requirement anymore, and privacy is protected since the AI model can be trained to only collect specific types of information. \n   \n This project breaks the mold of traditional communication methods, taking cutting edge (pun intended) technology and merging museum stories with Edge AI: Azure Percept, therefore pushing the boundaries for future exhibits/displays. \n   \n We are planning to place three Azure Percept DK in the museum’s main gallery. We want to monitor three important artifacts and measure the dwell time visitors spend with each artifact. We can, in turn, make changes to the artifacts and study changes in dwell time. Some of the data we could monitor includes: change in artifact height, change in artifact labeling, movement of artifacts to a different space in the museum gallery, and replacement of one artifact with another from the collection. As the museum staff learns about how AI can measure dwell time, we want to survey the staff on further applications of Edge AI: Azure Percept in the museum. By understanding what artifacts attract a visitor’s interest, and which ones they want to spend more time with, museums can create exhibits that will generate greater foot traffic and loyal patrons. We look forward to presenting our project at museum conferences nationwide! \n   \n To learn more about Azure Percept, visit these resources: \n \n Learn about Azure Percept \n Learn about Azure Percept Technical Documentation \n Learn more about our customer and partner stories \n \n   \n Microsoft Build 2022 Key Sessions \n \n Microsoft Build Into Focus: Preparing for the metaverse \n Embrace digital transformation at the edge with Azure Percept \n \n   \n GTC Spring 2022 Key Sessions \n \n Session: Transforming AI and ML at the edge with Microsoft and NVIDIA \n Embrace digital transformation at the edge with Azure Percept \n 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your Field Service Management with Azure Percept and HoloLens","conversation":{"__ref":"Conversation:conversation:3167006"},"id":"message:3167006","revisionNum":15,"uid":3167006,"depth":0,"board":{"__ref":"Blog:board:IoTBlog"},"author":{"__ref":"User:user:1136719"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" \n   \n   \n Learn how to integrate the Azure Percept DK device with Azure IoT for a field service management solution!  ","introduction":"","metrics":{"__typename":"MessageMetrics","views":3580},"postTime":"2022-02-17T08:00:00.046-08:00","lastPublishTime":"2022-02-17T08:00:00.046-08:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Microsoft Technology Centers has partnered with Kagool to demonstrate an end-to end field service management solution combining Azure Percept DK, AI, IoT, Cognitive Services and Mixed Reality with a physical Lego model. \n   \n Using Azure Digital twins, Mixed Reality and Azure Percept DK we digitalized the physical Lego Excavator into a digital presence, combining real-time telemetries and integrating a full end-to-end field service management solution. Real-time performance monitoring can be analyzed globally from a central location allowing you to reduce downtime and the cost of maintenance, optimize performance, reduce TCO and extend the life of your assets. Trend analysis enables intelligent servicing and predictive maintenance, allowing you to take preventative action before failures occur. \n   \n In this article our focus is to demonstrate how you can use the Azure Percept DK to integrate an end-to-end field service management solution and overlay the telemetries on a Digital Twin using Mixed Reality. \n   \n Overview of the solution \n   \n The architecture diagram below covers all the services we have used for implementing the field service management solution. The data which is being gathered by Azure Percept DK is being sent to IoT Hub where the Azure function is being triggered automatically (Event grid) to update the properties in Azure Digital Twins (ADT). In ADT, we rebuilt the global Lego asset deployments in a digital replica showing you exactly where each asset is deployed. Unity (Game Engine) will retrieve the properties based on custom queries sent to ADT using REST APIs and overlay telemetries on a 3D model. \n   \n \n   \n Below is a video on how the digital twin responds to the real-time Azure IoT ingested telemetries (as viewed from HoloLens 2 device): \n   \n \n   \n Implementation of the Solution \n   \n For you to get started, you will need to complete some prerequisites. You will need to set up your Azure Percept DK, create and configure an IoT Hub resource, train an AI model using Custom vision and lastly, set up your Function app instance to be able to move the data between services. If you need any help getting started, please look at the following resources: \n   \n Prerequisites: \n Set up the Azure Percept DK \n IoT Hub instance \n Custom Vision instance \n Function app instance \n Azure digital twin instance \n Unity project configured using Mixed Reality Toolkit \n   \n Steps of implementation: \n   \n \n Connect Azure Percept DK to IoT Hub. IoT Hub acts as a gateway for the IoT devices by collecting and then distributing the data accordingly. We have connected Azure Percept DK to IoT hub using the provided wizard. Below, we have attached a screenshot after configuring the device using the default settings: \n \n \n   \n   \n \n Create and train an AI model using Custom Vision and deploy it on the device using Azure Percept Studio. As an example, we have trained an AI object classification model to detect the color of different marbles as shown in the image below: \n \n \n   \n   \n \n Create an Azure Digital Twin instance and define the endpoints. We have used a layered approach by grouping each device based on its location (Continent – Region – City – Lego Asset). Example: \n \n \n   \n   \n   \n \n Configure an event grid-based Azure Function.  This will automatically update the Azure Digital Twin properties by the telemetry received by IoT hub. \n \n   \n \n Deploy your function to the Function App instance. We have used Visual Studio to write the code required for the function to run and deployed it using the publish feature as shown in the screenshot below: \n \n \n   \n   \n \n Go back to IoT hub, navigate to Events tab, create a new event grid schema subscription and select the previously created Azure Function as an endpoint. This is to ensure that all incoming messages are being routed to the right endpoint. After creating the event route, the main page should look like the screenshot below: \n \n \n   \n \n Configure your Unity Project to automatically update the data on the 3D Model. Using Azure Signal R, we can detect when a property is updated in Azure Digital Twins. Also, we used the powerful Azure Digital Twins APIs to send queries and retrieve metadata from ADT. \n \n   \n \n Configure the Unity project to display the ADT values. To enhance the user experience, we have displayed the Lego CAD model along with a few GUI elements: \n \n \n Left Panel: Listing out all the globally deployed assets and their status’ \n Middle Panel: The real-time IoT telemetries that come from Azure Digital Twins \n Right Panel: To initiate a work order back to SAP (ERP System) to replace a faulty component \n \n \n   \n Final thoughts \n   \n We have demonstrated to you how easy it is to integrate the Azure Percept DK with HoloLens. With this field service management solution, you can easily monitor your assets remotely from anywhere around the world and predict if a part is about to become defective to prevent any potential downtime or loss. This solution can be implemented in situations where humans cannot physically reach the asset location, or it would be a real danger for them to do so. \n   \n The benefits of this solution are many, we outline just a few below: \n   \n \n Remotely diagnose a performance problem anywhere in the world, create an inspection order, deploy maintenance personnel, and order replacement parts through seamless connectivity with your ERP system \n Predict what failures are likely to occur and in what circumstances to intelligently set maintenance intervals, reduce downtime and save costs \n Develop new commercial models based on pay-per-use through continuous ingestion storage and transformation of big data at high velocity \n Centrally monitor performance against pre-set tolerances to identify problems, take preventative action and reduce downtime. \n \n   \n Get started with Azure Percept DK: \n Purchase Azure Percept DK \n Azure Percept DK documentation \n Create a no-code vision solution in Azure Percept Studio \n   \n Get started with Mixed Reality: \n Purchase Microsoft Hololens 2 \n Develop mixed reality applications \n Mixed Reality Documentation \n   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