Glossary term
Glossary term
Foundations
The process of determining the ideal parameters (weights and biases) comprising a model. During training, a system reads in examples and gradually adjusts parameters. Training uses each example anywhere from a few times to billions of times.
See Supervised Learning in the Introduction to ML course for more information.
Created for this library
An ML platform team schedules training jobs nightly so production models are always built on the freshest approved data.
A research team logs every training run to a tracking system so experiments can be compared and resumed.
An ML platform team treats training as a reproducible pipeline with versioned datasets, code, and configurations.
Definition source: Google for Developers Machine Learning Glossary | Creative Commons Attribution 4.0 License