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Teachable Machine
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AI Agents (412)

Teachable Machine Verified Tool

Train simple image, audio, and pose classification models in a browser, then test and export them for projects.

Last Update: August 18, 2026

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Starting price Free

Tool Information

Teachable Machine is a free browser-based experiment from Google Creative Lab that makes basic machine-learning classification accessible without requiring coding. Users collect examples into labeled classes, train a model, test it with new inputs, and export it for use in websites, apps, classroom projects, and prototypes.

It supports image classification from files or a webcam, audio classification from short recordings, and pose classification from camera examples. Exported models are real TensorFlow.js models that can run in JavaScript environments, with additional formats and examples for tools such as p5.js, Glitch, Node.js, Coral, and Arduino workflows.

Training can run locally in the browser, allowing webcam or microphone examples to remain on the device in supported workflows. A small demonstration model is not production-ready by default: accuracy depends on balanced examples, representative environments, testing, bias review, accessibility, consent, and deployment constraints.

F.A.Q (20)

It is a free web tool for collecting examples, training simple image, audio, or pose classifiers, testing them, and exporting the resulting model.

Yes. Google provides the browser tool and learning materials without a paid subscription.

No for collecting examples, training, and basic testing. Coding is usually needed to integrate an exported model into a custom site, app, device, or workflow.

It supports image, audio, and body-pose classification projects. It is not a general text generator or large-scale custom model platform.

Supported training can run locally in the browser so camera and microphone examples do not need to leave the device. Review export and hosting choices separately.

Yes. Image and supported audio workflows can use prepared files in addition to live capture.

Models can be exported for TensorFlow.js and supported deployment formats, either downloaded or hosted for use by a project.

Yes. TensorFlow.js exports can run where JavaScript is supported, using the generated model files and integration code.

The official site provides examples and export guidance for selected hardware and tools, with compatibility depending on project type and format.

It may have learned background, lighting, speaker, camera, distance, or other accidental signals rather than the intended class. Add diverse representative examples and retest.

There is no universal number. Use balanced classes and enough varied examples to represent real lighting, angles, users, devices, noise, and edge cases.

Not without rigorous expert development, validation, governance, and applicable regulatory controls. A classroom prototype should never be treated as a validated diagnostic or safety system.

It can learn visual patterns in supplied examples, but identity and biometric uses create serious consent, privacy, bias, and legal risks and should generally be avoided in casual projects.

The official web product can be reached from the main homepage. Confirm any browser, extension, desktop, mobile, or API requirements there.

Integration availability varies by plan. Connect only necessary services and review requested permissions and data access.

Team suitability depends on seats, workspaces, roles, collaboration, audit history, and administrative controls.

Review current billing terms for cancellation timing, continued access, refunds, renewals, and data export or deletion.

Support channels and response times vary by plan. Confirm documentation, email, chat, onboarding, and priority support on the official site.

AI products can change features, models, pricing, and limits frequently. Recheck the official homepage before making a purchase decision.

Start with a limited representative task, define acceptance criteria, compare the output with a trusted baseline, and expand only after review.

Pros and Cons

Pros

  • Completely free to use
  • No coding required for basic training
  • Runs in a web browser
  • Supports image classification
  • Supports short audio classification
  • Supports body-pose classification
  • Live webcam and microphone capture
  • File upload supports prepared datasets
  • Immediate testing after training
  • Exports real TensorFlow.js models
  • Can host or download exported models
  • Works with JavaScript creative-coding tools
  • Useful tutorials and classroom examples
  • On-device training can protect captured examples

Cons

  • Limited to relatively simple classification tasks
  • Not designed for production-scale model management
  • Small datasets overfit easily
  • Backgrounds can become accidental signals
  • Class imbalance reduces reliability
  • Pose accuracy depends on camera framing
  • Audio accuracy changes with noise and microphones
  • No built-in fairness guarantee
  • Browser and hardware performance limit training
  • Exported projects still require development work
  • Model behavior needs testing outside the training setup
  • Sensitive biometric examples require consent
  • No automatic monitoring after deployment

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Overview

Pricing
Free

Applicable Tasks

GoogleCloud AutoML MachineLearning DataIngestion Prediction ImageProcessing

Before you subscribe

  • Check the currency, billing period, automatic renewal, usage limits and seats at checkout — prices and promotions change.
  • App-store pricing can differ from website pricing by country, device and tax.
  • Share only authorised data, and read the provider's encryption, retention and model-training terms first.
  • AI output can be incomplete or wrong. Verify facts, citations, calculations and code before you publish or ship.
  • Commercial usage rights vary by plan, provider terms and jurisdiction.
  • None of these tools replaces a qualified professional for medical, legal or financial decisions.

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