Google Teachable Machine is a free, browser-based tool for training a small machine-learning model without writing code. You provide labeled examples of images, sounds or body poses, click Train, test the result with new examples and export the model for a website, app or hardware project. The current interface identifies itself as release 2.4.16.
What Google Teachable Machine actually does
Teachable Machine is a supervised classification tool. Instead of creating a general-purpose chatbot or image generator, you define categories (called classes), supply examples for each category and let a neural network learn to distinguish them. A project might classify two objects, recognize several sounds or tell whether a person is performing one pose or another.
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Google describes the service as a way to “train a computer to recognize your own images, sounds, & poses.” Training takes place in the browser, making the tool suitable for classroom demonstrations, hobby experiments and quick prototypes rather than large production AI systems.
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| Project type | Examples you provide | Capture options | Typical output |
|---|---|---|---|
| Image | Photos grouped into classes | Upload files or use a webcam | Browser, website or app classifier |
| Sound | One-second audio clips grouped into classes | Upload files or record with a microphone | Browser, website or app sound classifier |
| Pose | Examples of body positions grouped into classes | Live camera capture | Interactive movement or gesture prototype |
The interface supports image files and webcam capture, plus one-second sound files or microphone recording. A webcam or microphone is optional: you can upload examples instead.
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How to train a model, step by step
- Open Teachable Machine in a modern browser. Choose an Image, Sound or Pose project.
- Create classes. Give each category a clear name, such as “mug” and “bottle,” or “clap” and “silence.”
- Gather examples. Upload files or capture them live. Include variation in lighting, angle, distance, background, speaker and volume where those factors will change in real use.
- Train the model. Click the training control and wait for the browser to process the examples on your device.
- Test with new examples. Show the camera an item that was not in the training set, play a fresh sound or perform the pose under different conditions. A confident training result alone does not prove that the classifier will generalize.
- Export the model. Use the Export option to download it or host it online, then connect it to a website, app or physical-machine experiment.
Why varied examples matter
The model learns patterns in the examples you label, including accidental patterns. If every photo of an object has the same background, it may learn the background rather than the object. If one sound is always recorded closer to the microphone, distance can become a shortcut. Representative examples and testing on genuinely new inputs make a classroom or hobby classifier more useful.
Keep classes balanced enough that one category does not dominate the training set, and include an “other” or neutral condition when your project needs to recognize that nothing relevant is present. These practices improve the experiment, but Teachable Machine does not publish a universal accuracy guarantee or current benchmark.
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Privacy and where training happens
Google’s 2017 launch description says Teachable Machine trains a neural network “right in your browser—locally on your device—without sending any images to a server.” Google’s 2019 Teachable Machine 2.0 announcement likewise says examples remain on the user’s computer unless the user chooses to save the project to Google Drive. Treat Drive saving as an explicit sharing and storage decision, especially for recordings of people or private surroundings.
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What you can build after export
- Web demos: Add the exported model to an interactive page that reacts to a camera view or sound.
- Apps: Package the model in a project that performs classification on supported devices.
- Physical experiments: Use a classification result to trigger lights, motors or sorting actions.
Google’s 2019 examples include Tiny Sorter, an Arduino-based project, and Teachable Sorter, which uses Coral hardware. An Arduino-compatible starter kit can extend the exercise, but no hardware is required for the browser workflow.
Choosing the simplest setup
| Your goal | Start with | Optional accessory |
|---|---|---|
| Recognize objects or images | Image project with uploaded photos | USB webcam for live capture |
| Recognize words, noises or instruments | Sound project with uploaded one-second clips | USB microphone for recording |
| Recognize gestures | Pose project with a camera | USB webcam if your computer lacks one |
| Control a physical prototype | Any suitable project, then export its result | Arduino-compatible starter kit or compatible maker hardware |
Common problems and fixes
The model works only in the training examples
Add more varied examples and retest with new inputs. Change backgrounds, angles, lighting, recording distance and timing so the model cannot rely on a single visual or audio cue.
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Predictions jump between classes
Capture clearer, more consistent examples and reduce overlap between classes. For sound, keep clips comparable in length and avoid unrelated noise; for poses, keep the camera view and framing usable.
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The camera or microphone is unavailable
Check the browser’s device permission prompt and operating-system input selection. You can also switch to uploaded image or sound files, so a live accessory is not required.
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You need a production-grade model
Teachable Machine is optimized for fast experiments and prototypes. It is not presented as a replacement for a large, general-purpose AI model, and Google does not publish a universal accuracy or adoption figure for it. For a high-stakes system, plan separate data collection, evaluation, monitoring and deployment work.
Why this free tool is useful
Teachable Machine removes the coding and infrastructure normally associated with a first machine-learning experiment. The essential loop—label examples, train, test on unseen inputs and export—can be completed in a browser, while local training offers a clear privacy advantage for projects that are not saved to Google Drive. Its limits are equally important: the model only knows the categories and conditions represented by your examples, and successful experimentation still depends on careful data and honest testing.
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