Understanding the Growth of Edge Computing

Edge computing is reshaping the data processing landscape, moving workloads closer to where data is generated to meet the growing demands of AI-driven low-latency applications and localized content. Predictions show that over 50% of data will be processed at the edge within a decade, creating significant opportunities for industries like manufacturing, engineering, and beyond. At Coevolve, as a tech-enabled managed services provider, we see edge computing as a natural extension of the software-defined infrastructure our clients already rely on. Many of our clients operate in verticals that are rapidly adopting edge solutions to enable real-time decision-making, reduce latency, and enhance operational efficiency. Edge computing, combined with SD-WAN, provides a resilient, agile, and scalable foundation for digital transformation, enabling businesses to: - Unlock new use cases: Industries like manufacturing can deploy IoT-driven predictive maintenance and automation solutions that require ultra-low latency and localized processing - Enhance network performance: Edge computing reduces dependency on centralized data centers, minimizing latency and network congestion for critical applications - Support hybrid workloads: SD-WAN offers the flexibility to integrate edge processing with cloud and on-prem environments, ensuring seamless application performance and security - Drive innovation: By decentralizing processing power, businesses can explore AI, AR/VR, and other emerging technologies with the confidence of a robust, software-defined infrastructure For our clients, this convergence of edge computing and SD-WAN is about creating a network and security architecture that accelerates innovation and supports their strategic initiatives. As these technologies mature, they will continue to redefine what's possible in digital transformation. This excellent article by Dan Meyer at SDxCentral offers an in-depth perspective on this shift towards the edge: https://lnkd.in/gBvxX-Mp

🔍 Why the Future of AI Is Local, Not Cloud-Based For the past decade, AI has largely meant cloud. Massive models, trained and deployed in centralized data centers, powered everything from smart assistants to industrial automation. But that era is shifting. According to SHD Group’s 2025 Edge-AI Report, a growing share of AI workloads is moving to the edge—on-device, real-time, and increasingly autonomous. By 2030, Edge-AI SoCs are expected to power 8.7B devices, with market revenue tripling to over $100B. Why the shift? It comes down to three factors: ✅ Better silicon: optimized for power-efficient inference ✅ Growing demand for privacy and responsiveness ✅ Emerging use cases like multimodal and agentic AI And it’s not just about more chips—it’s about smarter ones. CNNs still dominate, but transformer support is rapidly being baked into edge platforms. Multimodal AI models are redefining how devices interpret and act on context. Meanwhile, the software ecosystem is under pressure to keep up—with performance now measured in energy per inference, not just TOPS. This isn’t the end of cloud AI. But it is the beginning of something new: 📍 Intelligence that lives closer to the user 📍 Agents that understand context 📍 Devices that don’t just compute—they collaborate 👉 If you’re building for the edge, now’s the time to rethink your architecture, your models, and your metrics. 🔗 https://lnkd.in/gSirSubA

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

🚀 Is Edge Computing Still a Thing, or Has It Been Baked into the Enterprise Computing Cake? 🍰 As we dive deeper into 2025, it’s worth asking: is edge computing still a hot topic, or has it seamlessly integrated into enterprise computing? A few years ago, edge computing was the talk of the tech world—a revolutionary way to process data closer to where it's generated. It promised reduced latency, improved performance, and enhanced security. But where do we stand now? The reality is that edge computing is more relevant than ever. While many enterprises have adopted edge solutions, it hasn't just faded into the background. Instead, it's become a key ingredient in the modern enterprise landscape. Integration with Cloud Services: Edge computing complements cloud solutions, providing businesses with the best of both worlds. By processing critical data at the edge, companies can optimize bandwidth and improve response times without sacrificing the resources of the cloud. IoT Expansion: With the increasing proliferation of IoT devices, edge computing is vital. It allows businesses to harness real-time data from sensors and devices, facilitating faster decision-making and operational efficiency. Enhanced Security: As cybersecurity threats evolve, processing sensitive data at the edge helps mitigate risk. Businesses can implement localized security measures and reduce their vulnerability when data doesn’t have to travel back and forth to central servers. Emerging Trends: Innovations such as 5G technology are pushing the boundaries of what's possible with edge computing, creating even more opportunities for businesses to leverage this technology effectively. So while edge computing may not dominate headlines like it once did, it’s far from obsolete. It is, in fact, a foundational element of modern enterprise computing strategies. As we continue to innovate and adapt in this digital era, edge computing remains a critical player in optimizing performance and enhancing connectivity. Curious about the latest trends and insights? What are your thoughts on the role of edge computing in today’s enterprises? Let’s discuss! 💬 https://lnkd.in/eQfZEVJR #EdgeComputing #EnterpriseIT #CloudComputing #IoT #DigitalTransformation

𝗙𝗿𝗼𝗺 𝘁𝗵𝗲 𝗰𝗹𝗼𝘂𝗱 𝘁𝗼 𝘁𝗵𝗲 𝗲𝗱𝗴𝗲. 𝗕𝗿𝗶𝗻𝗴𝗶𝗻𝗴 𝗱𝗮𝘁𝗮 𝗰𝗹𝗼𝘀𝗲𝗿, 𝗻𝗼𝘁 𝗳𝗮𝗿 𝗮𝘄𝗮𝘆, 𝗶𝘀 𝘁𝗵𝗲 𝗻𝗲𝘄 "𝗵𝗼𝗹𝘆 𝗴𝗿𝗮𝗶𝗹." As the volume of data from #IoT devices is projected to reach a staggering 73.1 ZB by 2025, transferring this data from its source to a central #datacenter or #cloud for processing is becoming increasingly inefficient. Edge computing is gaining significant traction with #AI, which can intelligently process data at the edge, enhancing speed, latency, privacy, and security, revolutionizing how we handle and utilize information. AI model discussions have changed in the past year. Smaller, more focused models are replacing large models with many parameters. Efficiency methods like quantization, which reduces the precision of numbers in a model, sparsity, which removes unnecessary parameters, and pruning, which removes superfluous connections, are used to reduce the size of these models. These smaller models are cheaper, easier to deploy, and explainable, achieving equivalent performance with fewer computational resources. The smaller models can be applied in numerous task-specific fields. Pre-trained models can be adjusted for task performance using inferencing and fine-tuning, making them ideal for edge computing. These minor variants help with edge hardware deployment logistics and suit specific application needs. In manufacturing, a tiny, specialized AI model can continuously analyze machine auditory signatures to identify maintenance needs before a breakdown. A comparable model can monitor patient vitals in real-time, alerting medical workers to changes that may suggest a new condition. The impact of AI at the edge is not a mere theoretical concept; it's reshaping the very foundations of industries and healthcare, where efficiency and precision are of utmost importance. With its staggering 15 billion connected devices in the manufacturing sector, every millisecond lost in transferring data to the cloud for processing can have tangible consequences, from instant flaw detection to quality control. In healthcare, where the decentralization of services and the proliferation of wearable devices are becoming the norm, early analysis of patient data can significantly influence diagnosis and treatment. By eliminating the latency associated with cloud computing, AI at the edge enables faster, more informed decision-making. This underscores the urgency and importance of adopting these technologies, as they are not just the future but the present of data processing. The global #edgecomputing market is not just a statistic; it's a beacon of hope, a world of new opportunities, and improved performance across all industries, thanks to the transformative potential of edge AI. The future is bright and promising for these technologies, as the graph from Statista below suggests, instilling a sense of optimism and excitement about their possibilities.