Standard 3 stops to get here · leads to 1
Robustness
A model's ability to maintain performance under distribution shifts, adversarial attacks, or noisy inputs.
Your route here
3 stops · basics first
- Dataset ✓ understood
A collection of data examples used for training, validating, or testing machine learning models.
- Training Data ✓ understood
The examples a model learns its weights from, kept separate from the validation and test data used to check how well it generalizes.
- Overfitting ✓ understood
When a model fits its training data too closely, noise included, so it scores well on examples it has seen and poorly on new ones.
- Robustness · you are here ✓ understood
Where it sits
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Evaluation Out-of-Distribution Data that differs significantly from the training distribution, where models often perform poorly or unreliably. Shipping AI Adversarial Attack Intentionally crafted inputs designed to fool AI models into making incorrect predictions, exposing vulnerabilities. Shipping AI Certified Robustness Provable guarantees that a model's prediction won't change within a specified input perturbation. Training Data Augmentation Creating variations of training data through transformations (rotation, cropping, noise) to improve model generalization. Shipping AI Data Drift Changes in input data distribution over time that can degrade model performance in production.