It is also important to ensure that the benchmark environment is similar to the business production environment, he said, and to document areas where network, compute, storage, inputs, outputs, and contextual augmentation of the benchmark environment differ from the production environment.
Further, make sure that the model tested matches the model that is available for preview or for production, Park advised. It is common for models to be optimized for a benchmark, without revealing deep detail into the cost or time required for the training, augmentation, or tuning going into that optimization.
Ultimately, “businesses seeking to conduct a competitive evaluation of AI models can use benchmarks as a starting point, but really need to scenario test in their own corporate or cloud environments if they want an accurate understanding of how a model may work for them,” Park emphasized.
