data_entry_ops · saas · workflow
Deque uses Labelbox Model Diagnostics and Catalog to improve ML model performance 5%+ and cut labeling spend by over 50%
Deque's ML team spent about 10 minutes annotating each webpage screen and relied on disparate open-source annotation tools hacked together with Jupyter Notebooks and Google Sheets to manually calculate model metrics, making dataset curation and error diagnosis a disjointed, labor-intensive process.
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 · Webpage screen annotation
It takes an average of about 10 minutes to fully annotate a single webpage screen.
Tools used
LabelboxModel DiagnosticsCatalogJupyter NotebooksGoogle Sheets
Outcome
Using Labelbox Model Diagnostics and Catalog, Deque filtered out about one-third of less trustworthy data points, raised model performance by 5%+, and reduced annual labeling spend and needs by over 50%.
What failed first
Before Labelbox, the team had to manually visualize model predictions and calculate metrics themselves, and data selection for poorly-performing classes required tedious, untargeted collection.
Results
Time savedabout 10 minutes
Volume5%+
Cost replacedover 50%
Grounding & classification
Source type: vendor customer story
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anomaly detectioncomputer visionquality inspectionknowledge basefailure mode describedhuman review describedmetric backednamed customersource backedtools describedworkflow describedsoftwareaccuracy improvementcost reductiontime savedvendor customer storydata entry opsquality assurancedata sync enrichmenthuman review queue