quality_assurance · saas · workflow
Ancestry uses Labelbox to achieve weekly ML model iteration cycles for genealogical data extraction
Ancestry's data science team needed more efficient ways to label training data for ML models decoding census records and historical artifacts, and struggled to involve domain experts meaningfully because data scientists owned the entire labeling task end-to-end while the review process was painfully slow.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Census and artifact labeling need
The data science team seeks more efficient ways to decode census data and train ML models faster.
Tools used
LabelboxAnnotateBoost
Outcome
The Ancestry team reached a weekly model iteration cycle, saved time collaborating with domain experts, and can now train and test new models in record time using Labelbox's data engine.
Results
Time savedsave time
Running sincebefore November 2022
Grounding & classification
Source type: vendor customer story
28 fields verified against source quotes.
data extractiondocument aiquality inspectionform submissionknowledge basefailure mode describedhuman review describednamed customerproduction runtime claimedtools describedvendor confirmedworkflow describedsoftwareaccuracy improvementcycle time reductiontime savedvendor customer storydata entry opsquality assurancedocument to recordhuman review queue