Teachable Machine is a web-based machine learning tool that enables users to train custom classification models without writing code. Through a visual interface, users can collect training samples using their webcam, microphone, or local files to categorize images, sounds, and body poses. The platform handles model training directly in the web browser, ensuring on-device privacy during data processing. Once trained, models can be tested in real time to verify accuracy and behavior. Teachable Machine supports exporting completed models to formats such as TensorFlow.js and TensorFlow Lite, as well as providing hosting options for web deployments. It also offers direct integration options for physical computing platforms, including Arduino and Coral hardware. The tool is designed for educators, students, accessibility researchers, creative developers, and hardware hobbyists who need a quick way to prototype machine learning applications or teach core artificial intelligence concepts. Users can apply these models to projects ranging from interactive digital art installations and assistive communication triggers to agricultural quality control and educational ethics lessons.
Target audience: Best for: Educators and students, Accessibility researchers, Hardware hobbyists, Creative developers
Pricing: Unknown · Categories: Developer Tools
Tags: developer tools, low-code/no-code, transcriber
Teachable Machine allows users to train machine learning models to classify images, sounds, or body poses directly in a web browser. Users gather data through webcams, microphones, or file uploads, organize samples into distinct classes, and train the model with a single click. The tool provides real-time testing, model hosting, and export capabilities for external applications and physical hardware.
Teachable Machine supports model exports to TensorFlow.js for browser-based projects and TensorFlow Lite for mobile or embedded systems. It also provides hosted model links for quick web integration. Additionally, trained models can be connected to physical computing devices, including Arduino boards and Coral hardware, allowing developers to trigger physical actions based on image, sound, or pose inputs.
Teachable Machine processes data and trains models locally within the user's web browser. Samples captured from webcams or microphones, as well as uploaded files, remain on the local device during the training and testing process rather than being sent to external servers. This on-device approach allows students, researchers, and creators to experiment with machine learning without exposing sensitive visual or audio data.
Teachable Machine is designed for educators, students, hardware hobbyists, accessibility researchers, and creative developers. Because it requires no programming background or machine learning expertise, teachers use it to introduce artificial intelligence and data ethics concepts. Creative technologists and accessibility specialists use it to build gesture-driven art projects, prototype assistive communication devices, or create sensors for physical computing platforms.