Training data teaches a machine learning model. The model sees examples and learns the relationship between inputs and outputs. For a spam filter, the training data is emails labeled spam or not spam. For an image classifier, it is photos labeled with their contents. For a language model, it is vast amounts of text. The model adjusts its internal parameters to minimize the difference between its predictions and the correct labels. The quality and quantity of training data determine how well the model performs.

Training data has several requirements. It must be representative of the real-world data the model will encounter. A model trained only on daytime photos fails on nighttime photos. It must be labeled accurately. Mislabeled examples teach the model wrong patterns. It must be large enough to capture the variation in the data. Too little data leads to overfitting, where the model memorizes the training examples instead of learning general patterns. It must be free of bias, or the model will learn and amplify that bias. Training data is also a privacy concern. Models trained on personal data may memorize and reproduce it. Regulatory frameworks increasingly require documentation of training data sources and processing. The data is not just an input. It is a liability and an asset. The model is only as good as the data it learned from.

Training data requirements

• Representative — reflects real-world variation
• Labeled — accurate ground truth
• Large — enough examples to generalize
• Unbiased — no systematic skew
• Documented — provenance and processing known

Training data is the teacher. The model learns what the data shows. Bad data teaches bad lessons.