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Model training, simplified

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Train your model with one click

Turn your labeled data into a trained model without friction. Connect Labelbox to your preferred model training cloud provider or your custom model training service via webhooks or the Python SDK and launch training jobs all from the Labelbox UI. Simplify your pipeline with a single low-code integration.

Version data at every iteration

Track model experiments and automatically version labels, data splits, data rows, and model parameters across each model run iteration. Reproduce model results by restoring previous versions of data without 3rd party tools or custom scripts.

Manage train, validation and test data splits

Ensure that your model is constantly learning from an accurate representation of real-world scenarios. Curate training, validation, and testing data splits to evaluate performance and prevent overfitting. No more saving data to manually managed folder structures.
Coming soon

Easily configure and track parameters

Streamline the model development process by configuring and tracking your essential model training parameters all in one place alongside your training data and data splits.

Find and fix errors in your model and labeled data

Systematically find and fix errors in your model and labeled data so you can focus your next iteration of labeling on data that will more dramatically improve performance. Visualize model predictions alongside labeled data to help pinpoint errors.

Track essential metrics across model versions

Evaluating and comparing model performance has never been easier. Perform differential diagnosis between model predictions and labels or a previous model version. Track essential performance metrics like precision and recall across model versions.

Image - Hazel Erickson

Hazel Erickson
Developer, Computer Concepts Limited

Labelbox’s Model Diagnostics allows me to easily visualize our trained models and their performance, as well as recognize patterns in our training data which affects model performance. Having these diagnostic features on the same platform as where we manage our training data streamlines our processes so we can spend more time focusing on addressing model errors and building a high quality training data set to improve our model performance.

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