Training Data

Related Terms

• Machine Learning

  Machine learning is a branch of AI in which systems learn patterns from data to make predictions or decisions. The behaviour comes from training on examples rather than from rules written by hand.

• Data Anonymization

  Data anonymization is transforming data so that individuals cannot reasonably be identified from it, even when it is combined with other available information. Teams use it to share health or customer records with researchers, or to build realistic test datasets, without exposing personal details.

• Data Labeling

  Data labeling is adding correct answers, such as categories, tags, boxes or ratings, to raw examples. The labelled data is then used to train or evaluate a machine learning model.

• Data Poisoning

  Data poisoning is an attack that corrupts the data used to train, fine-tune or ground an AI model. The model then learns hidden flaws, biases or backdoors chosen by the attacker.

• Data Quality

  Data quality is how well data fits its intended use, judged on qualities such as accuracy, completeness, consistency, timeliness, validity and uniqueness. Teams track it with automated checks, for example flagging duplicate customer records or missing order dates before they reach a finance report.

• Feature Store

  A feature store is a system that stores, documents and serves the input features used by machine learning models. It keeps them consistent between training and live predictions.