To produce a high-quality model that fits business requirements, data scientists need to experiment with all kinds of datasets, data processing techniques, and training algorithms. To build and ship the best model, they spend a significant amount of time conducting these experiments.
A variety of artifacts (datasets and model files) and metadata are produced from model training experiments. The metadata may include model algorithms, hyperparameters, training metrics, and model versions, which are very helpful in analyzing model performance. To be useful, this data must be persistent and retrievable.