# Labelling prioritization

### **Why Label Selection Matters**

In many real-world workflows, teams maintain a **large pool of unlabeled data**, but only a limited labeling budget. Periodically, a small subset of that data is selected for annotation — often based on heuristics, random sampling, or gut feeling.

But not all samples are equally valuable. Labeling redundant, uninformative, or already well-represented data leads to:

* Slow improvements in model performance
* Wasted annotation effort
* Missed edge cases and critical blind spots

The real challenge is **deciding what to label** — selecting the most informative, high-impact samples from a sea of unlabeled inputs.

#### **How Tensorleap Helps Prioritize What to Label**

Once a model has been evaluated within the platform, Tensorleap enables you to **prioritize labeling with a single click**. It analyzes the model’s latent space to rank samples from the unlabeled pool based on their potential value to the model.

This means you can:

* Focus labeling effort on what matters most
* Avoid over-labeling redundant samples
* Make measurable progress with each new annotation cycle

You can choose how many samples to retrieve — or let Tensorleap recommend a number based on the current model state.

📸 *\[Insert screenshot placeholder: prioritized sample selection UI]*

## A Full Labelling prioritization Walkthrough

Coming Soon


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