What is Data Augmentation?
Creating variations of existing training data to increase dataset size and improve model performance.
Why It Matters
Data augmentation helps models learn more effectively from limited data by exposing them to more variations.
Real-World Example
Flipping, rotating, and adjusting the brightness of training images so a model sees more varied examples.
“Understanding terms like Data Augmentation matters because it helps you have better conversations with developers and make smarter decisions about your software. You do not need to be technical. You just need to know enough to ask the right questions.”
Related Terms
Training Data
The dataset used to teach an AI model patterns and knowledge during its initial training.
Synthetic Data
Artificially generated data used to train AI models when real data is scarce or sensitive.
Overfitting
When an AI model memorises its training data too closely and performs poorly on new, unseen data.
From definition to deployment
Knowing the term is step one. Using it in something real is the rest.