Edge AI Inference Engines Compared: TFLite vs ONNX Runtime and How to Choose for Your Project
A practical guide to choosing the inference engine — the most important decision when deploying AI models to edge devices. We compare the strengths, weaknesses, and performance of TFLite, ONNX Runtime, and related engines, and give clear, s
[Practical Guide] Leave the Cloud Behind: Deploying Edge AI Models with TFLite and ONNX
Feeling the limits of cloud-based AI? This guide lays out a practical roadmap for actually deploying trained models to edge devices like Raspberry Pi and Jetson, covering the A-to-Z process of compressing and optimizing models with TFLite a
Compressing Models for Edge AI: Quantization, Pruning, Distillation, and a Jetson Deployment Flow
Finding it hard to deploy AI models on low-power edge devices outside the cloud? This guide walks you systematically from the core principles of model compression (quantization and pruning) through a practical deployment pipeline using TFLi
Industry-Specific AI Success and Failure Case Analysis: A Roadmap from Theory to Real-World Application
Heading into AI adoption with vague expectations? This guide deeply analyzes real success and failure cases in core industries such as finance and manufacturing, covering technical limits and regulatory compliance, and delivers a practical
Designing a Real-Time Streaming Pipeline for Edge AI: Ingestion, Transport, and Edge Inference
A complete architecture blueprint for running AI in ultra-low-latency settings such as autonomous driving and smart factories, without depending on the cloud. From MQTT and model compression to smart data-sync strategies, it covers practica
Beyond the Cloud to Smartphones: A Complete Guide to Edge AI for On-Device LLM Implementation
Tired of API call costs and latency? This article provides a complete, step-by-step guide to the full architecture and model lightweighting techniques for implementing Edge AI and on-device LLMs, so you can master practical deployment metho
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.
[Part 2] Make Your Edge AI Models This Lightweight: A Practical Optimization Guide from Quantization to Deployment
Hitting performance bottlenecks when moving AI models from the cloud to edge devices? This guide covers model compression (quantization and pruning) with TFLite, ONNX, and more—from theory through hands-on optimization and deployment on Jet
Edge AI Deployment, Part 1: Model Compression and On-Device Inference
Cloud AI is powerful, but latency, privacy, and cost make it a poor fit for real-time devices. This first installment covers when to choose edge AI, how to pick hardware and runtimes, and what to verify before you ship on-device inference.
[Part 1] Connecting the Heart of the Shop Floor to AI: A Complete Guide to AI-OT Gateway Design Methodology
The key to bridging the technical disconnect on shop floors where cutting-edge AI models coexist with 30-year-old PLCs is the AI-OT gateway. This guide presents practical design methodology that smart-factory builders need—from defining the
[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.
Beyond the Cloud to the Edge: An In-Depth Analysis of Modern Architecture Patterns and Tech Stacks for On-Device LLM Deployment
A practical guide to on-device LLM deployment that overcomes cloud API limits and delivers ultra-low latency. From quantization, ONNX, and TensorRT to production architecture patterns, it lays out a roadmap you can apply immediately.