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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Intel

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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Tide

A UK based fintech company, used Snorkel to match receivable invoices from the mobile app with incoming transactions.

PROBLEM

Tide needed to label matching invoices with transactions that required investing highly paid subject matter experts’ time in hand-labeling historical data.

SOLUTION

Used Snorkel to programmatically label data, extract information, and harness business knowledge by creating labeling functions.

RESULTS

Achieved 97.6% accuracy to detect transactions made for a particular invoice. Created training data programmatically replacing 1000 hours of hand labeling.

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0
Days to create training dataset & deploy model
0%
ML model accuracy
0M
Invoices processed

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