Utilizing Edge Computing

Edge computing is making a serious comeback in manufacturing—and it’s not just hype. We’ve seen the growing challenges around cloud computing, like unpredictable costs, latency, and lack of control. Edge computing is stepping in to change the game by bringing processing power on-site, right where the data is generated. (I know, I know - this is far from a new concept). Here’s why it matters: ⚡ Real-time data processing: critical for industries relying on AI-driven automation. 🔒 Data sovereignty: keep sensitive production data close, rather than sending it off to the cloud. 💸 Cost control: no unpredictable cloud bills. With edge computing, costs are often fixed and stable, making budgeting and planning significantly easier. But the real magic happens in specific scenarios: 📸 Machine vision at the edge: in manufacturing, real-time defect detection powered by AI means faster quality control, without the lag from cloud processing. 🤖 AI-driven closed-loop automation: think real-time adjustments to machinery, optimizing production lines on the fly based on instant feedback. With edge computing, these systems can self-regulate in real time, significantly reducing downtime and human error. 🏭 Industrial IoT (and the new AI + IoT / AIoT): where sensors, machines, and equipment generate massive amounts of data, edge computing enables instant analysis and decision-making, avoiding delays caused by sending all that data to a distant server. AI is being utilized at the edge (on-premise) to process data locally, allowing for real-time decision-making without reliance on external cloud services. This is essential in applications like machine vision, predictive maintenance, and autonomous systems, where latency must be minimized. In contrast, online providers like OpenAI offer cloud-based AI models that process vast amounts of data in centralized locations, ideal for applications requiring massive computational power, like large-scale language models or AI research. The key difference lies in speed and data control: edge computing enables immediate, localized processing, while cloud AI handles large-scale, remote tasks. #EdgeComputing #Manufacturing #AI #Automation #MachineVision #DataSovereignty #DigitalTransformation

#AI doesn’t always need the cloud. When split second decisions are made in the OR or in a diagnostics lab, AI needs to be operating on the edge.   At Cyient, we’re driving that shift with #TinyML by optimizing deep learning models to run fast and efficiently on the edge.   Why it matters: Edge computing is projected to reach $43B by 2030, growing at 38% CAGR and On-device AI can cut clinical response times by up to 70%. Cyient TinyML unlocks real-time intelligence where connectivity is limited and speed is important.   Our latest whitepaper breaks down how we applied techniques like quantization and pruning to compress models (VGG16, MobileNet, and more) across use cases in radiology, dermatology, and even fashion retail.   You’ll find a tested blueprint for delivering high-performance AI at the edge, without compromising accuracy.   📄 Get the whitepaper: https://lnkd.in/gKcTN5hx   Building edge-ready AI? Let’s make it faster, lighter, and smarter—together.   #TinyML #EdgeAI #HealthcareAI #AIOnTheEdge #IoTDevices #Cyient #AIOptimization #DigitalEngineering

🩺 What if rural clinics could run AI diagnostics… without the internet? Sounds impossible? It’s not. It’s called edge infrastructure — and it might be the biggest game changer the health sector has seen in decades. Here’s the reality: Most AI in healthcare today relies on cloud infrastructure — which means it’s expensive, data-heavy, and completely reliant on stable internet. 🌍 In cities? Maybe. 🏥 In rural clinics? Good luck. But with edge technology, something radical happens: ✅ AI runs locally — right on-site ✅ No need for constant internet ✅ Real-time processing of images, feedback, or patient triage ✅ Massive cost reduction ✅ More access to care for more people Imagine this: 📡 A clinic with no broadband, but still able to: Run visual diagnostics for maternal health Triage patients automatically during high-volume hours Store and sync health records safely — even offline That’s not a distant future. That’s edge computing, done right. 💡 If we’re serious about affordable, scalable healthcare, then edge infrastructure isn’t optional — it’s essential. We can’t wait for connectivity to catch up. We need to bring intelligence to the last mile — today. Agree? Have a use case in mind? I’d love to hear it.👇 #HealthTech #EdgeComputing #DigitalHealth #AIinHealthcare #SmartClinics #LastMileInnovation #AfricaHealthTech #OfflineAI #InfrastructureInnovation #TechForGood

Edge Computing processes data closer to its source rather than sending it to a central data center or cloud, using devices and systems located at the "edge" of the network, near users or data-generating devices. Here’s how Edge Computing can enhance application responsiveness and overall efficiency: ▪ Latency Reduction: Edge Computing significantly reduces latency by processing data closer to its origin, enhancing the speed and responsiveness of applications - crucial for rapid-response scenarios like financial transactions and autonomous vehicles. ▪ Real-World Applications: Industries benefit from Edge Computing through real-time machinery monitoring in manufacturing, personalized shopping experiences in retail, immediate patient monitoring in healthcare, and optimized traffic management in transportation. ▪ Improved Responsiveness: By processing data locally, Edge Computing ensures faster responses, decreasing the delay between request and response, which is essential for applications requiring quick decision-making and action and thus enhancing overall efficiency. ▪ Bandwidth Efficiency: With Edge Computing, there is less need to send large amounts of data to the cloud, which reduces bandwidth usage and lowers the associated costs, leading to more efficient data handling and transmission. ▪ Reliability: Edge Computing offers continuous operation even if cloud connectivity is disrupted, ensuring uninterrupted service delivery and making systems more resilient to network failures, thereby improving overall reliability. ▪ Enhanced Security: Processing sensitive data locally at the edge provides greater control and security, reducing the risk of data breaches during transmission and ensuring that critical information remains protected and confidential. ▪ Integration Challenges: Integrating Edge Computing with existing systems can be complex, requiring careful planning and execution to ensure seamless operation and to leverage the full benefits of this technology in a business environment. ▪ Technology Consideration: Successful Edge Computing implementation involves evaluating necessary technologies, such as capable edge devices, robust software platforms for device management, and reliable connectivity solutions for secure and fast data transfer. ▪ Implementation Strategy: Businesses should identify the most beneficial use cases for Edge Computing, develop a clear adoption plan, ensure IT staff is well-trained, and choose reliable technology partners to support the deployment process effectively. Adopting Edge Computing can significantly enhance your application responsiveness, leading to improved efficiency, reduced costs, and enhanced security. #EdgeComputing #NetworkEfficiency #RealTimeData Ring the bell to get notifications 🔔

Discover how Edge AI brings machine learning capabilities directly to devices, enabling real-time data processing and reducing latency in applications like autonomous vehicles and IoT. 𝗜𝗻 𝘁𝗵𝗶𝘀 𝗮𝗿𝘁𝗶𝗰𝗹𝗲, 𝘆𝗼𝘂’𝗹𝗹 𝗹𝗲𝗮𝗿𝗻: ➡️ What Is Edge AI and Why It Matters ➡️ How On-Device AI Works (Compared to Cloud AI) ➡️ Real-World Use Cases Across Key Industries ➡️ How Edge AI Enables Faster, Private Decision-Making ➡️ Challenges in Edge AI Deployment ➡️ Market Growth, Trends & Industry Insights ➡️ Future Outlook for Edge-Based Intelligence 𝗙𝗔𝗤𝘀: 𝟭. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗘𝗱𝗴𝗲 𝗔𝗜 𝗮𝗻𝗱 𝗵𝗼𝘄 𝗱𝗼𝗲𝘀 𝗶𝘁 𝘄𝗼𝗿𝗸? Edge AI refers to running AI models directly on local devices (like smartphones, cameras, sensors), enabling real-time data processing without relying on cloud servers. 𝟮. 𝗛𝗼𝘄 𝗱𝗼𝗲𝘀 𝗼𝗻-𝗱𝗲𝘃𝗶𝗰𝗲 𝗔𝗜 𝗿𝗲𝗱𝘂𝗰𝗲 𝗹𝗮𝘁𝗲𝗻𝗰𝘆? By processing data locally, on-device AI eliminates the need to send information to the cloud, resulting in instant decision-making and near-zero delay. 𝟯. 𝗪𝗵𝗮𝘁 𝗮𝗿𝗲 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗲𝘅𝗮𝗺𝗽𝗹𝗲𝘀 𝗼𝗳 𝗘𝗱𝗴𝗲 𝗔𝗜? Examples include self-driving cars detecting obstacles, smart cameras doing facial recognition, wearables monitoring health in real time, and factory robots optimizing production. 𝟰. 𝗛𝗼𝘄 𝗱𝗼𝗲𝘀 𝗳𝗲𝗱𝗲𝗿𝗮𝘁𝗲𝗱 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗶𝗺𝗽𝗿𝗼𝘃𝗲 𝗽𝗿𝗶𝘃𝗮𝗰𝘆? Federated learning trains AI models across devices without sharing raw data, keeping personal information local while still improving model performance globally. 𝟱. 𝗪𝗵𝗶𝗰𝗵 𝗶𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗲𝘀 𝗯𝗲𝗻𝗲𝗳𝗶𝘁 𝗺𝗼𝘀𝘁 𝗳𝗿𝗼𝗺 𝗘𝗱𝗴𝗲 𝗔𝗜? Industries like healthcare, automotive, manufacturing, finance, and smart cities benefit the most by gaining faster insights, improving safety, and reducing cloud costs. 𝟲. 𝗪𝗵𝗮𝘁 𝗮𝗿𝗲 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 𝗶𝗻 𝗱𝗲𝗽𝗹𝗼𝘆𝗶𝗻𝗴 𝗘𝗱𝗴𝗲 𝗔𝗜? Key challenges include limited device power, model size constraints, security risks, and lack of standardization across hardware platforms. 📌 𝗣𝗦: Want to explore how Edge AI can bring real-time intelligence to your business or product? Let’s connect here: https://lnkd.in/d7FR8yK2 or learn more at https://lnkd.in/dSrCAq45 #EdgeAI #RealTimeAI #EdgeComputing #AIinHealthcare #SmartDevices #FederatedLearning #AIInnovation #MachineLearning

🔎 Many industrial operators face the same challenge: "How can we use AI to detect anomalies early enough to prevent unplanned downtime?" That’s a question I often hear in conversations with customers. During a recent visit with Daniel Mantler, our product manager for edge computing, he shared a use case that addresses exactly this challenge. As we all know by now, AI is no longer rocket science. But getting it into real life industrial applications still seeems to be. And that's where our team of experts developed a lean and fast to adapt setup that uses local sensor data to detect for example vibration, temperature, or anomalies directly at the machine. A lightweight machine learning model runs on an edge device and identifies deviations from normal behavior in real time. Because the data is processed on-site, latency is minimal and data sovereignty is maintained. Both aspects are critical in many industrial environments. But the real value lies in the practical benefits for operators: Faster reaction times, reduced dependency on external infrastructure, and the ability to integrate AI into existing systems without needing a team of data scientists. What are your thoughts on integrating ML into edge architectures? I’m keen to hear your thoughts. Let’s use the comments to share perspectives and learn from one another. For those who want to dive deeper into the technical setup and learnings, here’s the full article: 🔗 https://lnkd.in/e8Z5HMCH #artificialintelligence #machinelearning #edgecomputing

Cloud engineers shouldn't sleep on edge computing this year. With the explosion of IoT and 5G, data needs to be processed closer to where it’s created. The benefits? Faster responses, reduced latency, and lower network strain. Industries are adopting edge computing at a fast pace: 𝗠𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗶𝗻𝗴 -> Real-time equipment monitoring to reduce downtime. 𝗨𝘁𝗶𝗹𝗶𝘁𝗶𝗲𝘀 -> Managing smart grids with real-time data from IoT devices. 𝗥𝗲𝘁𝗮𝗶𝗹 -> Personalizing customer experiences and improving in-store operations. 𝗕𝗮𝗻𝗸𝗶𝗻𝗴 -> Faster fraud detection and secure data processing at ATMs. Edge computing is becoming a core part of cloud infrastructure. By 2028, edge spending is expected to hit $378 billion. The problem? Edge computing isn’t without its pain points: - Decentralized devices are more vulnerable, especially in remote areas. - Ensuring low latency across distributed networks can be difficult. - Managing and scaling multiple edge environments adds complexity. To address these issues, companies are focusing on: - Solutions that use advanced encryption and zero-trust frameworks. - Machine learning models for real-time insights and maintenance. - Seamlessly connecting edge, multi-cloud, and on-premise environments. A primary solution to consider is 𝗘𝗱𝗴𝗲 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁. It simplifies edge operations through: Automated Workflows -> Streamlines deployment, scaling, and monitoring. Centralized Control -> A single control point for managing distributed nodes. Real-Time Monitoring -> Ensures optimal resource use and task execution. Popular tools for edge orchestration include K3s, MicroK8s, KubeEdge, and ioFog. These lightweight frameworks help manage resources across edge and cloud, making scaling easier and reducing complexity. My advice? Start building your edge strategy now. Don’t wait for problems to appear. Plan for security, orchestrate efficiently, and keep your systems nimble. #EdgeComputing #CloudEngineering #IoT #5G #AI Stay ahead in cloud engineering. ➕ Follow me for more tips.