data_entry_ops · manufacturing · workflow
Blue River Technology automates ML data curation and labeling at scale with Labelbox, accessing datasets from 1B+ images within minutes
Blue River Technology's ML model training lifecycle took several weeks due to slow, manual data curation and labeling processes that worsened as data volumes grew exponentially, while ML engineers spent excessive time on data infrastructure instead of model development.
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 · Petabyte-scale data ingestion
As technology was deployed on more machines, data collected grew exponentially, making rapid ingestion of petabytes imperative to scaling up.
Tools used
LabelboxLabelbox CatalogKubeflow · partnerDatabricks · partner
Outcome
Blue River Technology's ML teams can access updated, curated datasets within minutes from over a billion images, and the model-assisted labeling workflow cut human labeling time and costs in half, allowing teams to focus on training and maintaining computer vision models.
Results
Time savedwithin minutes
Volumeover a billion images
Cost replacedcut human labeling time and costs in half
Grounding & classification
Source type: vendor customer story
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computer visionknowledge searchquality inspectionhuman review describedmetric backednamed customerproduction runtime claimedtools describedworkflow describedagriculturemanufacturingcost reductioncycle time reductionemployee productivitytime savedvendor customer storydata entry opsquality assuranceextract classify routehuman review queue