quality_assurance · workflow
Galileo surfaces and fixes 6.5% of ML dataset errors in 10 minutes with 7.24% model performance improvement
ML datasets in production have critical quality issues — including empty samples, garbage samples, labeling errors, and class overlap — but most modern tools focus on changing model configurations or collecting more data rather than addressing root data quality flaws.
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 · Dataset loaded into Galileo
Benchmark datasets are analyzed using Galileo to surface crucial errors and ambiguities within minutes.
Tools used
GalileoDistil-BERT
Outcome
Galileo identified and fixed 1163 dataset errors (6.5% of 18,000 samples) within 10 minutes, producing a 7.24% overall model performance improvement.
Results
Time saved10 minutes
Volume1163
Grounding & classification
Source type: technical build writeup
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anomaly detectiondata extractionquality inspectionknowledge basehuman review describedmetric backedsource backedtools describedworkflow describedsoftwareaccuracy improvementcycle time reductionerror reductiontime savedtechnical build writeupdata entry opsquality assurancehuman review queuemonitor detect alert