Datasets And Machine Learning

What are Datasets in Machine Learning?

Datasets are the structured collections of examples used to train, validate, and test machine learning models. Their coverage, labeling quality, and leakage controls largely determine what a model can learn safely.

Strong dataset practice includes clear splits, documentation (datasheets), bias review, and monitoring for poisoning or drift after deployment.

Related reading on Giskard

• Data poisoning on enterprise LLM apps

Audit training sets for poison and leakage — Before you scale a dataset, probe for poisoning, PII leakage, and distribution shift that will show up as model risk later. Learn more: Data poisoning on enterprise LLM apps.

Authoritative reference: TensorFlow Datasets