Google Research has introduced GIST, a smart-sampling algorithm designed to make large-scale machine learning cheaper by selecting a small, representative subset of a dataset for training. The approach is aimed at the data-preparation stage, where processing and labeling huge volumes of data can dominate the cost of building models.
The post positions GIST as the next stage in smart sampling, building on earlier work in the area. It argues that the method can reduce the cost of data preparation while preserving model quality relative to training on the full dataset. The post does not claim GIST eliminates the need for careful data engineering, but rather that it makes sampling smarter and more efficient.
Because there is only one source, there is no independent comparison to make. The claims about cost savings and quality retention come directly from Google Research and have not been verified by outside benchmarks in the source material.