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Reading performance metrics off a leaderboard is one thing; getting your hands dirty with the actual model is quite another.

Sure, benchmarks are cool, but they don’t give you the feel or the intuition of how a model actually works. To get that, you’ve got to hack around with the model and throw real-world prompts at it — like you’d do in day-to-day tasks.

Harness GenAI and popular ML models for pre-labeling, interactive labeling, and model evaluation. Advancements to the ML backend and a new examples library makes it easy to create powerful automations for Label Studio workflows.

In the world of machine learning, data labeling plays a central role in training accurate and reliable models, and properly preparing your data for labeling sets the foundation for the success of your machine learning projects.

Learn how to set up an audio labeling project in Label Studio and walk through the latest features for audio labeling available in the Label Studio 1.7 release.

Label Studio Enterprise has a new annotations UI that’s more ergonomic, efficient, and ready to support larger, more complex tasks with dozens to hundreds of regions.

Learn how to leverage Superconductive's Great Expectations in concert with Label Studio to test your data.