The Heart of Edge AI: A Guide to Building Data Pipelines for Real-Time Stream Data (Part 3)
Edge AI model performance is decided at the data preprocessing stage. This guide presents a concrete architecture and workflow for turning real-time sensor and stream data into high-quality features a model can use immediately.
Industrial AI Integration Architecture Guide, Part 3: Edge-Cloud Hybrid Deployment
Shop-floor manufacturing AI cannot wait for a response: it needs <50ms inference, offline operation, and drift detection. This part covers low-latency ONNX inference, batching for 10–20× throughput, Isolation Forest predictive maintenance,
Industrial Site AI Integration Architecture Guide, Part 2: Real-Time Anomaly Detection and Predictive Maintenance
Unlike typical web traffic, industrial data arrives as thousands of sensor readings per second, with millisecond timestamps and proprietary protocols from decades-old equipment. Building on Part 1’s AI-OT gateway, this article walks through
[Roadmap Revealed] How to Protect Factory Data Without External Transmission: Implementing IIoT Anomaly Detection with Edge AI-Based LLMs
Learn how to run AI analysis on the factory floor without sending sensitive plant data to the cloud. This post lays out a practical roadmap for real-time anomaly detection in OT/IT environments using Edge AI and lightweight LLMs.