Teachable Machine

Teachable Machine is a web-based tool designed to democratize access to machine learning. Its primary function is to allow any user, regardless of prior programming or data science experience, to create custom AI models quickly and intuitively. Through a simple visual interface, it facilitates the training of systems capable of recognizing specific patterns in different types of files or real-time data inputs.

The operation of Teachable Machine is structured into a three-step process: collection, training, and export. The user defines different categories or "classes" and provides examples by uploading files or through live captures. The platform uses advanced artificial intelligence technologies to process the data, allowing training to be completed in a matter of seconds. Once the model is generated, it can be instantly tested within the browser to verify its accuracy before being deployed in a real-world environment.

This application stands out for its technical versatility, offering three main recognition modes:

• Image recognition: Identifying objects, faces, or colors using a computer camera or by uploading local images.
• Sound recognition: Classifying environmental noises, specific words, or short audio snippets.
• Pose recognition: Detecting body position and limb movement in space through computer vision.

One of the biggest advantages of Teachable Machine is its focus on privacy and technical integration. Processing can be performed entirely locally, meaning that camera or microphone data does not have to leave the user's device. Additionally, the resulting models are highly compatible with various development ecosystems. They can be exported for use in websites, mobile apps, or hardware projects, and are compatible with libraries and platforms such as TensorFlow, ML5.js, p5.js, node.js, Framer, Glitch, and Arduino.

The use cases for this no-code AI tool are extensive and practical. It is ideal for educators, designers, and developers looking to prototype solutions quickly. It has been used to create assistive communication devices, video game controllers that operate via paper gestures, and even physical machines capable of automatically sorting objects. Its ability to connect the digital and physical worlds through boards like Coral makes it an essential resource for experimentation in the field of applied artificial intelligence.