Edge Analytics Development

Edge is not a trend; it’s an architecture shift. From $10B in 2023 to $50B+ by 2033... ...the growth isn’t driven by hype. It’s driven by physics. Because once you move from 100 ms to 20 ms, apps feel usable. But to cross 5 ms? You need to compute at the baseband, not the core. Here’s how to engineer edge sites that deliver deterministic low latency.. ...the kind autonomous vehicles, high-frame-rate AR, and critical IoT actually depend on: 1️⃣ Deploy true micro-edge, not retrofitted closets. Use prefabricated, hardened SmartMod™ units from Schneider Electric. Each is factory-integrated for power, cooling, fire, and control. Drop next to STC, Du, or Airtel 5G towers. Size them in 50 kW increments, enough for MEC, AI inference, or on-prem cloud functions. 2️⃣ Terminate fibre and power before you lift a panel. Edge buildouts fail when backhaul and power provisioning lag site readiness. Lock dual feeds (utility + genset), reserve dark fibre with SLA-bound loop latency. Tie telemetry into a regional NOC using EcoStruxure™ IT Expert. 3️⃣ Architect for adversarial environments. At edge, risk profiles flip. You’re no longer behind seven enterprise firewalls. Implement zero-trust gateways at entry points. Segment IoT ingress from control networks. Deploy biometric access control per rack, not just facility. 4️⃣ Design for thermal density and burst load. Run average loads at 65–70% to preserve thermal headroom. Plan cooling for non-linear spikes from MEC caching or edge GPU workloads. Active airflow control, rear-door heat exchangers, or liquid-ready chassis, depending on density. 5️⃣ Treat orchestration as a control system, not a dashboard. With EcoStruxure™, power, cooling, access, and IT converge into a decisioning plane. Don’t just monitor, let the system act. Use real-time data to preempt failure, not just alarm on it. This isn’t edge as a PoC. This is production-grade, SLA-bound, carrier-integrated infrastructure. 5G gives you bandwidth. Edge gives you responsiveness. Without both, your low-latency promise doesn’t land. Ready to design for 5 ms? Let’s draw your first edge map.

After many years in the #software #engineering industry, I’ve learned that innovation thrives at the intersection of ambition and constraint. Today, I’m thrilled to share a milestone from my latest proof-of-concept (PoC): Deploying a “Qwen 2.5” powered #AI agent on a local laptop with just 16GB RAM and no GPU, using the cutting-edge “GGUF format” and “llama.cpp” framework. This project exemplifies how strategic architecture and optimization can democratize AI, even in resource-limited environments.    🚀 Why This Matters for Enterprise AI? While #cloud based LLMs dominate the spotlight, “local deployment” unlocks transformative opportunities:   👉 Zero-compromise #data privacy: Critical for industries where sensitive data never leaves the device. 👉 Real-time responsiveness: Eliminating network latency enables use cases like on-device #analytics, edge #IoT control, and offline-first applications. 👉 Cost-effective scalability: Bypassing cloud costs for prototypes and small-scale deployments accelerates ROI for #startups and enterprises alike.   🚀As a seasoned solutions architect, I thrive on challenges where hardware limitations demand creativity. Here’s how I engineered success:    1️ Model Strategy: Precision Over Power: Why Qwen 2.5? Its balance of performance and adaptability made it ideal for CPU-only inference. The GGUF format (a successor to GGML) was critical, enabling efficient memory #management through quantization without sacrificing accuracy.   2️⃣ llama.cpp: The Unsung Hero of Edge AI: Leveraging llama.cpp (optimized for GGUF) allowed me to bypass GPU dependencies entirely. Its lightweight C/C++ core and multi-threading support transformed a standard laptop into a capable AI inference engine.   3️⃣ Architecture Designed for the Edge: Built a microservices-based agent that interfaces with local APIs, processes real-time data streams, and delivers sub-second response times—all within 16GB RAM.   🚀 Lessons for Fellow Architects & Engineers: 👉Constraints Breed Innovation: Limited RAM and no vRAM forced us to rethink memory allocation, leading to a leaner, more efficient architecture. 👉Future-Proof Flexibility: Our design supports seamless hybrid scaling, local execution today, cloud burst tomorrow. 👉Open Source as an Equalizer: Tools like llama.cpp and GGUF democratize access to SOTA models, empowering teams without massive budgets.    💡 What’s Next? This PoC isn’t just a technical win—it’s a blueprint for ethical and accessible AI. Imagine deploying regulatory-compliant AI agent without costly infrastructure.    🃏 Why I shared this? To inspire engineers and leaders to see hardware limitations not as barriers, but as catalysts for smarter design. So I hope the above is useful to you! should you need any further information or if I can be of assistance, please do not hesitate to contact me 👉Mohammed BENNAD   #Innovation #DataPrivacy #softwarearchitecture #digitaltransformation #softwaredesign #softwareengineering

The blog post is relevant to AI, Machine-/Deep-Learning (ML/DL), IoT (Internet of Things), Signal Processing, Control Systems, Embedded System Engineers as well as Students and Enthusiasts, on how to combine cross-technology features to deploy a deep learning model trained in #Matlab for operation on GPU-powered edge devices. Also, check out the MathWorks solution in the link that's posted in the comments: • "MATLAB and #Simulink for Edge AI" Excerpts: --------- Why is Edge AI Important? Let’s assume we run a very large electric generation provider with thousands of assets. AI technology can assist in many aspects, from predictive maintenance to automation and optimization. These assets span hydro-electric, nuclear, wind, and solar facilities. Within each asset, there are thousands of condition-monitoring sensors. Each location will likely require several servers, equipped with robust hardware, such as a strong CPU and a powerful GPU. While sufficient compute capacity to support Edge AI is now realistic, several new hurdles emerge: How do you effectively administer and oversee a massive server fleet with limited connectivity? How do you deploy AI model updates to the hardware without having to send a team to physically tend to the hardware? How do you ensure a new model can run across a multitude of hardware and software configurations? How do you ensure all model dependencies are met? The workflow we will show you at GTC will demonstrate how we are starting to address these hurdles. Workflow key steps: • The engineers perform transfer learning using MATLAB. They perform model surgery on a pre-trained deep learning model and retrain the model on new data, which they can access using Domino’s seamless integration with cloud repositories. They also leverage on enterprise-grade NVIDIA GPUs to accelerate the model retraining. • The engineers package the deep learning model using MATLAB Compiler SDK and, then, use Domino to publish the model into an NVIDIA Fleet Command-compatible Kubernetes container. • The IT team loads the container into the company’s NVIDIA Fleet Command container registry using a Domino API. • Once configured, Fleet Command deploys the container to x86-based, GPU-powered factory floor edge servers. The model is then available where it is needed, with near-instantaneous inference. https://lnkd.in/gPFRKXYF

We've got a challenge to deal with, data at the edge. It's not an optional task but a necessary one. When we talk about edge computing, we often need data as well. But most of the time, this data is isolated in a database somewhere, maybe Postgres or MySQL. We need to bring that data closer to the edge for our edge computing application to work efficiently and quickly. One of the best things about edge is that it brings the computing process closer to your users. But if the data is too far away, even if your computing is close to the user, it still takes too long to fetch said data. So, we need to figure out how to get this data to the edge. One common method is simply caching the data. Fetch calls are made and the data is cached at the edge, ensuring a great experience. The cache can be pre-loaded and kept ready. There are even specific strategies to keep the data fresh. Other options are services like Terso, a globally distributed SQLite database. Terso's library can be integrated into your application to connect to the nearest uplink for the database. This brings data practically right next to your users for a fantastic experience. All these strategies need network access. Without it, there's no way to bring data into the system. With CloudFlare workers, a few more options come into play. They offer D1 and R2, distributed data-back systems which can help bring data closer to the edge, right where you need it. With PubNub, App Context can be used to introduce user data and business info right to the same region where your users are. The data preloads, and it's updated whenever it changes. This results in top performance. If something goes wrong and one region becomes unreachable for users, they can go to the next closest region. It's always there and accessible through an API, making edge tech easier to use. Finally, there's an option to handle this yourself. You could self-replicate data within your application, using an SQLite or a memory store with a disk sync to a centralized database. With this approach, you always copy data to the nearest location, where it's directly accessible to users. It's a similar strategy to caching data, but in a more structured manner, replicating the exact copy of your schema into the nearest data center to your users. #edgecomputing #datacaching #distributeddata