Federated Learning for Edge Devices
Federated learning for edge devices lets tiny gadgets learn together while keeping data private. This guide covers the basics, tools, and a smart‑home example.
TinyML on-device models
TinyML on-device models let tiny devices run AI locally for privacy, low latency, and low cost. This guide covers tools, design tips, and a sample wake word project.
AI Predictive Maintenance: A Practical Guide for Manufacturing
AI predictive maintenance uses sensor data and machine‑learning models to forecast equipment failures, cutting downtime and maintenance costs. This guide covers the workflow, tools, challenges, and real‑world case studies for manufacturers.
Federated Learning for Edge Devices
Federated Learning for Edge Devices keeps data on the device, reduces bandwidth, and builds better AI models across a fleet of devices. This guide explains the concepts, architecture, and real-world use cases.
AI Smart Building Energy Optimization: Turning Architecture into a Power‑Saving Machine
AI smart building energy optimization transforms a building’s sensors and devices into a self‑learning system that cuts energy costs, reduces peak demand, and improves occupant comfort.
AI Digital Twin for Cities: Turning Data into Smart Urban Futures
An AI digital twin for cities transforms real‑time data into a predictive, 3‑D model that helps planners optimize traffic, energy, and emergency responses.
AI Autonomous Drone Swarm: Building Smarter Disaster Response and Industrial Inspection
AI autonomous drone swarm lets many small drones work together to spot fires, inspect plants, and search for survivors faster than a single vehicle.
AI Ocean Monitoring: Keeping the Seas Clean and Productive
AI ocean monitoring harnesses sensors, drones, and AI to spot pollution, track fish, and keep our seas clean and productive.
TinyML: Deploying Machine Learning on Microcontrollers for Smart Sensors
TinyML lets you run neural networks on tiny microcontrollers, enabling smart sensors that keep data local and save power. This guide walks through a voice‑activity demo on ESP32‑S3 and covers best practices.
Edge AI for IoT: Deploying Models on Low‑Power Devices
A practical guide to building, optimizing, and deploying Edge AI for IoT on low‑power devices. Learn how to train, convert, and run models on Raspberry Pi, ESP32, and more.