Training (model training)

The phase where model parameters are optimized on data or feedback before deployment: distinct from inference at run time.

Training adjusts weights and other learnable parameters using a dataset and a loss or reward signal. At scale this can take weeks of GPU clusters and careful data curation: supervision, privacy, bias, and documentation all matter.

After training, models are often validated, sometimes fine-tuned further, then used for inference.