Case Study: Ancestry accelerates historical record decoding with Labelbox

Ancestry speeds weekly model iteration with Labelbox

Ancestry, the genealogy company, faced a bottleneck in its machine learning pipeline. Their data science team needed to extract data from historical records like census documents faster, but producing high-quality training data and incorporating feedback from domain experts was slow and inefficient. This hindered their shift to a data-centric approach and optimization of their MLOps workflow.

To solve this, Ancestry adopted Labelbox's platform, utilizing its model-assisted labeling and collaboration features. This solution combined automated workflows with direct expert feedback within native editors, accelerating signal generation. Using Labelbox as a data engine, the team achieved a weekly model iteration cycle, saved significant time in collaboration, and was able to train and test new models in record time while maintaining high data quality.

Ancestry