Technology proven in production at some of the world’s leading organizations

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Case studies —

Technology developed and deployed with some of the world's leading organizations

Early versions of Snorkel Flow's core technology have been developed in partnership with — and deployed at — some of the world’s most sophisticated ML organizations, including several deployments publicly described in peer-reviewed case studies:
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Google
Google used Snorkel to replace 10-100K hand-annotated labels in key ML pipelines
PROBLEM
Content, product, and event classification problems change too fast to hand-label, even with significant annotation budget
SOLUTION
We deployed early versions of Snorkel Flow's core technology with three high-impact teams at Google, repurposing many organizational resources as labeling functions
RESULTS
Hours of labeling function development replaced 10-100K+ hand labels, significantly impacting the bottom line and acceleration of ML solution adoption
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0K+
Hand labels replaced
0%
Improvement by repurposing resources
0M+
Labels in < 30 min.
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Google
Intel used Snorkel to replace a high-cost, high-latency crowdsourcing pipeline and accelerate sales and marketing agents
PROBLEM
Rapidly changing sales goals make social media monitoring difficult to maintain
SOLUTION
We deployed a prototype version of Snorkel Flow ("Snorkel Osprey") to replace months-long crowdworker processes with cheap and fast template-based programmatic labeling
RESULTS
Better performance and major cost savings in Sales & Marketing and Advanced Analytics
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0
Months of crowdworker labels replaced
+0.5
Precision percentage points
+0.5
Coverage percentage points
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Stanford Medicine
Researchers at Stanford Medicine used Snorkel to label medical imaging & monitoring datasets, replacing person-years of hand labeling with several hours of using Snorkel
PROBLEM
Labeling training data for triaging models takes person-months to person-years of radiologist time
SOLUTION
We deployed a cross-modal Snorkel pipeline, matching or exceeding the performance of painstakingly gathered manual labels in hours
RESULTS
Currently being tested for deployment in Stanford & Department of Vetaran Affairs (VA) hospital systems
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0
Person-months of labeling replaced
0%
ROC AUC Performance
0K+
Images labeled in minutes
Top U.S. Bank
A top U.S. bank uses Snorkel Flow to quickly build AI applications that classify and extract information from their documents.
PROBLEM
The bank estimated that, for a time-sensitive use case, hand-labeling data would take over a month.
SOLUTION
With Snorkel Flow, the team produced a solution that was over 99% accurate in under 24 hours.
RESULTS
The resulting AI application could be quickly and easily adapted to new problems and business lines.
0.1%
Snorkel Flow Accuracy
< 0hrs
From problem start
> 0K
# Documents processed

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