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For its documented browser workflow, Google Teachable Machine is designed to capture examples and train models on your device. The product page says webcam and microphone data can remain on-device, and the legacy FAQ says training happens locally rather than storing images on Google servers.
That protection applies to capture and training—not automatically to every later step. Exporting, hosting, embedding, or sharing a model introduces separate files, URLs, labels, and access decisions that you must control.
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The short answer: private during local training, separate risks after export
Teachable Machine runs its documented image, audio, and pose training workflow in your web browser. Google Creative Lab says the tool can be used entirely on-device, “without any webcam or microphone data leaving your computer.” Its legacy FAQ answers the question “Are my images being stored on Google servers?” with: “No. All the training is happening locally on your device.”
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Those statements describe the examples you capture or select and the training computation performed in the browser. They do not establish that every browser artifact, diagnostic record, extension, or later model publication is private. Once you export a model or give another application its model URL, you are dealing with a new data-access surface.
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| Stage | What the official material establishes | Privacy question that remains |
|---|---|---|
| Collect image examples | Files or webcam captures can be used in the browser; on-device use is supported. | Can other software on the computer see the camera, files, or screen? |
| Collect sound examples | Audio files or microphone recordings can be used, with an on-device option. | What permissions, extensions, or operating-system processes have access? |
| Collect poses | Pose examples are among the supported input types. | Do not assume pose capture has broader privacy guarantees than the documented browser workflow. |
| Train | The FAQ says training happens locally; the community repository says TensorFlow.js trains and runs models in the browser. | No product-specific retention schedule is published for every browser-side artifact. |
| Export or publish | Official snippets load model files and metadata from a model URL. | Who can reach that URL, and what labels or other metadata does it reveal? |
How Teachable Machine processes examples
Images
You can provide image files or capture examples from a webcam. In the local workflow, the browser receives the camera stream or selected file, uses it to create training examples, and performs the model training on the device. The product page’s on-device statement specifically addresses webcam data leaving the computer; the FAQ separately states that images are not stored on Google servers for this training process.
Audio
Audio can come from files or a microphone. The same on-device option is presented for microphone use, so a recording used for training need not be sent to a remote training service. A microphone permission is still a device-level decision: the browser, an extension with permission, malware, recording software, or someone with access to the computer could potentially observe the input independently of Teachable Machine.
Poses
Pose capture is listed alongside image and audio inputs. The public statements focus on local browser processing and explicitly mention webcam and microphone data; they do not publish a separate pose-specific retention policy. Treat pose examples with the same discipline as other sensitive camera-derived material.
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Browser execution
Google Creative Lab’s Teachable Machine community repository describes the implementation this way: “Under the hood we use Tensorflow.js, a library for machine learning in Javascript, to train and run the models you make in your web browser.” That explains why training can occur without sending each example to a cloud training endpoint: the JavaScript and machine-learning work execute in the browser context.
What “local” does—and does not—mean
Local processing means the documented capture and training computation can stay on the computer instead of being uploaded as a training dataset to Google’s servers. It is a narrower claim than “nothing related to this activity is ever collected.” The available product statements do not promise zero telemetry, zero browser storage, or a guaranteed deletion time for every temporary artifact.
No Teachable Machine-specific published retention period was identified for local examples, browser caches, crash logs, or telemetry. Therefore, do not make a stronger promise than the documentation supports. Training locally is a meaningful privacy advantage, but it is not a formal guarantee about every layer of the device or browser.
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Other software on the device remains in scope
- A browser extension with broad permissions may read page content, interact with uploads, or observe a tab.
- Malware, remote-administration tools, or screen-recording software can access camera, microphone, files, or the display regardless of Teachable Machine’s training location.
- Another person using the computer may be able to open downloaded files, browser profiles, or exported model folders.
- Operating-system camera and microphone indicators show active permissions, but an indicator does not audit every process that can access a file or screen.
Exporting a model changes the privacy question
Training locally and publishing a trained model are separate actions. The official image export snippet loads model.json and metadata.json from a model URL. The audio example likewise loads model files over HTTPS. Those files must be hosted somewhere that the client code can reach.
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Metadata deserves attention
Class names and other metadata can disclose more than a model’s technical structure. A label such as a person’s name, medical condition, workplace, location, or confidential project can identify what the model was intended to recognize even if the original training files were never published. Use neutral labels when a model may leave your private workspace, and inspect metadata.json before sharing.
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Export documentation does not establish that original webcam frames or microphone recordings are automatically bundled into those files. It does establish that model files and metadata are fetched by URL, so you should review exactly what you host rather than assuming an exported package has no privacy implications.
A practical privacy workflow
- Decide whether the example is suitable for browser capture. Avoid using identifiable people, confidential conversations, private locations, or regulated information unless you have a clear reason and permission. Local training reduces transmission risk; it does not remove the need for lawful and ethical handling.
- Use the least sensitive input method. Upload a prepared file instead of leaving a camera or microphone active when a file will do. For live capture, close unrelated applications and remove other people from the frame or recording.
- Check browser permissions before collecting examples. Confirm which site has camera and microphone access, deny permissions you do not need, and watch the operating-system indicators while recording. Revoke the permission afterward if you will not use it again.
- Review the device environment. Disable unneeded extensions, lock the computer when unattended, and avoid collecting sensitive examples on a device you do not control. A clean browser profile can reduce accidental access by extensions, but it is not a substitute for endpoint security.
- Use non-identifying class names. Do not put names, account numbers, diagnoses, addresses, or confidential project terms in labels that could become part of exported metadata.
- Inspect every exported file and URL. Open the model folder or hosting location, check
model.jsonandmetadata.json, and verify the URL’s sharing permissions. Test access from a separate, unauthenticated browser window if the model is supposed to be restricted. - Share only what the application needs. If a model is for a private prototype, keep its files on access-controlled hosting and do not paste the URL into public source code or documentation. Remove hosted files when the project ends, according to the host’s controls.
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What Google’s broader privacy policy does not prove
Google’s general Privacy Policy discusses media from interactions with Google services and provides account retention and deletion controls. That broad policy should not be treated as proof that Teachable Machine uploads local training samples. The Teachable Machine-specific statements establish local browser training; they do not supply a universal retention or deletion commitment for every browser-side artifact.
Conversely, the local-training statements should not be expanded into a claim that Google collects no information connected with use of the site. The published material does not document a complete inventory of analytics, crash handling, caches, or other operational data for every browser and configuration. The accurate conclusion is limited: the documented examples and training computation can remain on the device.
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When local training is appropriate—and when to choose another setup
Local browser training is a reasonable fit when:
- You need a quick proof of concept using non-sensitive images, sounds, or poses.
- You can control the computer, browser profile, extensions, and network environment.
- You can keep the exported model and metadata behind appropriate access controls.
- The project can tolerate the absence of a product-specific deletion schedule for browser artifacts.
Use a more controlled workflow when:
- Examples contain health, financial, biometric, employment, educational, or other regulated information.
- You require a contractual data-processing agreement, auditable retention limits, or guaranteed deletion.
- You cannot secure the endpoint from extensions, malware, shared accounts, or screen-recording tools.
- The model must be distributed publicly but its labels or behavior could reveal confidential information.
For a different browser machine-learning tool, compare the same concrete properties rather than relying on a “private” label:
| Comparison axis | Question to ask |
|---|---|
| Training location | Does training run locally in the browser, on a vendor server, or in a hybrid workflow? |
| Input transmission | Do webcam, microphone, file, or pose inputs leave the device, and under what trigger? |
| Export reachability | Are model and metadata files private by default, or reachable through an unprotected URL? |
| Account and telemetry data | What account identifiers, analytics, diagnostics, or usage events are collected? |
| Retention and deletion | Is there a documented retention period and a way to request or verify deletion? |
Common mistakes and their fixes
“It is local, so I can publish anything.”
Fix: Treat publication as a new decision. Inspect model files and metadata, remove identifying labels, and restrict hosting access before sharing.
“A webcam cover makes the whole workflow private.”
Fix: A cover blocks the lens only. Revoke camera permission, manage microphone access separately, secure the endpoint, and review network and hosting behavior.
“Google’s general policy confirms what Teachable Machine stores.”
Fix: Use the product-specific local-processing statements for the training claim. Do not infer a complete retention schedule from a general policy.
“No original images are visible, so metadata does not matter.”
Fix: Read class names and other metadata as potentially identifying. Rename sensitive classes before export and limit who can fetch the files.
Bottom line
Google Teachable Machine’s documented capture-and-training workflow is designed to keep image, audio, and pose examples on your device while TensorFlow.js runs in the browser. That answers whether the normal training process automatically sends your webcam or microphone data to Google: the product documentation says it does not. Privacy becomes a separate engineering task when browser artifacts, device access, exported files, metadata, hosting, or shared URLs enter the picture.
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