data_entry_ops · manufacturing · workflow
NASA JPL uses Labelbox video annotation to train a life-detection ML model for spacecraft missions
The MLIA team's PhD ML researchers were spending time writing custom Java GUIs and manually sorting through data samples to create labeling solutions for each new project, which was a highly inefficient use of their time and slowed ML 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 · Trigger: label diverse microscopy data
The team sets about labeling a large, diverse set of data sourced from field water samples and lab-grown specimens.
Tools used
Labelboxdecision treesSVMs
Outcome
With Labelbox, the team achieved 5x the speed of customization and annotation and completed their most manual-intensive setup tasks in a day rather than the budgeted week, while also creating significant improvements in their model.
What failed first
The in-house approach required PhD-level ML researchers to build Java GUIs from scratch for each project, diverting their expertise away from model development.
Results
Time savedbudgeted a week to get it set up, but we were done in a day
Volume5x
Grounding & classification
Source type: vendor customer story
23 fields verified against source quotes, 1 dropped as unverifiable.
computer visiondata extractionfailure mode describedhuman review describedmetric backednamed customerproduction runtime claimedtools describedworkflow describedgovernmentcycle time reductionemployee productivitytime savedvendor customer storydata entry opsquality assuranceextract classify routehuman review queue