Definition
AI training data is the labeled dataset used to teach machine learning models how to identify patterns and generate predictions. It represents the “ground truth” against which models adjust their internal parameters.
Purpose
The purpose is to provide examples that guide algorithms to learn statistical relationships. It enables models to generalize from examples to unseen data.
Importance
- The quality of training data directly impacts model accuracy.
- Biased or imbalanced data produces unfair or unreliable models.
- Sufficiently large datasets improve generalization.
- Training data leakage into test sets compromises evaluations.
How It Works
- Define the prediction task and dataset requirements.
- Collect relevant raw data.
- Label or annotate the data with correct outputs.
- Split into training, validation, and test sets.
- Train the model to adjust weights based on the training data.
Examples (Real World)
- COCO dataset: annotated images for detection and segmentation.
- Common Crawl: large-scale web text dataset for pretraining LLMs.
- LibriSpeech: speech dataset for ASR training.
References / Further Reading
- Training Data for Machine Learning — IBM Research.
- ISO/IEC 23053: Framework for AI Systems Using ML — ISO.
- NIST AI Risk Management Framework — NIST.