Poolside's Model Factory treats training as a repeatable experiment with traceable inputs. Its engineering account describes preserving dataset versions and their processing history, then automatically evaluating models at configured intervals during training. Researchers can connect a result to the data and settings that produced it.
The system also streams training data instead of assembling one fixed combined dataset for every experiment. That allows researchers to change the proportions of different data sources between experiments or during a run. The intended benefit is quicker, more interpretable experimentation when both the training material and the model are changing.
