Case studies
Technology proven in production at some of the world’s leading organizations.
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Case study
Google used Snorkel to replace 100K+ hand-annotated labels in critical ML pipelines for text classification.
Read more
Problem
Content, product, and event classification problems change too fast to hand-label, even with significant annotation budget.
Solution
Google deployed early versions of Snorkel's core technology with three high-impact teams, repurposing many resources as labeling functions.
Results
Hours of labeling function development replaced 10-100K+ hand labels, significantly impacting the bottom line and accelerating of ML adoption.
6 months
of hand-labeling data replaced in 30 mins
52%
performance improvement
100k+
hand labels replaced with a programmatic approach
Case study
Google used Snorkel to replace 100K+ hand-annotated labels in critical ML pipelines for text classification.
Read more
Problem
Content, product, and event classification problems change too fast to hand-label, even with significant annotation budget.
6 months
of hand-labeling data replaced in 30 mins
Solution
Google deployed early versions of Snorkel's core technology with three high-impact teams, repurposing many resources as labeling functions.
52%
performance improvement
Results
Hours of labeling function development replaced 10-100K+ hand labels, significantly impacting the bottom line and accelerating of ML adoption.
100K+
hand labels replaced with programmatic approach
Case Study
Google used Snorkel to replace 100K+ hand-annotated labels in critical ML pipelines for text classification.
Read More
Problem
Content, product, and event classification problems change too fast to hand-label, even with significant annotation budget.
Solution
Google deployed early versions of Snorkel's core technology with three high-impact teams, repurposing many resources as labeling functions.
Results
Hours of labeling function development replaced 10-100K+ hand labels, significantly impacting the bottom line and accelerating of ML adoption.
6 Months
of hand-labeling data replaced in 30 mins
52%
performance improvement
100k+
hand labels replaced with programmatic approach
Case study
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.
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Problem
The bank estimated that, for a time-sensitive use case, hand-labeling data would take over a month.
Solution
The bank estimated that, for a time-sensitive use case, hand-labeling data would take over a month.
Results
The resulting AI application could be quickly and easily adapted to new problems and business lines.
99.1%
Snorkel Flow accuracy
<24hrs
from problem start
>250K
documents processed
Case study
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.
Read more
Problem
The bank estimated that, for a time-sensitive use case, hand-labeling data would take over a month.
99.1%
Snorkel Flow accuracy
Solution
With Snorkel Flow, the team produced a solution that was over 99% accurate in under 24 hours.
<24hrs
from problem start
Results
The resulting AI application could be quickly and easily adapted to new problems and business lines.
>250K
# documents processed
Case Study
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.
Read More
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.
99.1%
Snorkel Flow accuracy
<24hrs
from problem start
>250k
# documents processed
Case study
Apple
Apple built applications with an internal Snorkel-based system that answered billions of queries in multiple languages and processed trillions of records with up to 2.9x fewer errors.
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Problem
Apple needed a system that supported engineers facing contradictory or incomplete supervision data.
Solution
Apple built a solution called Overton which utilized Snorkel’s framework of weak supervision to overcome cost, privacy, and cold-start issues.
Results
Overton achieved a 12%+ bump in F1 score by going from 30K to 1M data labels.
12%
bump in F1 score
2.9%
fewer errors with Snorkel-based applications
32x
more labels generated
Case Study
Apple
Apple built applications with an internal Snorkel-based system that answered billions of queries in multiple languages and processed trillions of records with up to 2.9x fewer errors.
Read more
Problem
Apple needed a system that supported engineers facing contradictory or incomplete supervision data.
12%
bump in F1 score
Solution
Apple built a solution called Overton which utilized Snorkel’s framework of weak supervision to overcome cost, privacy, and cold-start issues.
2.9%
fewer errors with Snorkel-based applications
Results
Overton achieved a 12%+ bump in F1 score by going from 30K to 1M data labels.
32x
more labels generated
Case Study
Apple
Apple built applications with an internal Snorkel-based system that answered billions of queries in multiple languages and processed trillions of records with up to 2.9x fewer errors.
Read More
Problem
Apple needed a system that supported engineers facing contradictory or incomplete supervision data.
Solution
Apple built a solution called Overton which utilized Snorkel’s framework of weak supervision to overcome cost, privacy, and cold-start issues.
Results
Overton achieved a 12%+ bump in F1 score by going from 30K to 1M data labels.
12%
bump in F1 score
2.9%
fewer errors with Snorkel-based applications
32x
more labels generated
Case study
Fortune 500 biotech
A Fortune 500 biotech pioneer leveraged Snorkel Flow to extract critical chronic disease data from clinical trials, accurately processing 300K documents in minutes.
Read more
Problem
Building AI applications to extract entities requires high domain expertise and large amounts of labeled training data, which is expensive and time consuming.
Solution
With Snorkel Flow they built a custom model with 99.1% accuracy by adjusting label schema and re-labeling programmatically.
Results
With Snorkel Flow, this biotech giant programmatically labeled ~300K documents in minutes versus using manual labeling, all while saving $10M in costs.
$10M
saved on labeling for extraction
99.1%
accuracy on complex ML pipeline
1 day
vs. 1 year to adjust label schema
Case study
Fortune 500 Biotech
A Fortune 500 biotech pioneer leveraged Snorkel Flow to extract critical chronic disease data from clinical trials, accurately processing 300K documents in minutes.
Read more
Problem
Building AI applications to extract entities requires high domain expertise, and large amounts of labeled training data, which is expensive and time consuming.
$10M
saved on labeling for extraction
Solution
Used Snorkel Flow to build a custom model with 99.1% accuracy by adjusting label schema and re-labeling done in hours.
99.1%
accuracy on complex ML pipeline
Results
With Snorkel Flow, this biotech giant programmatically labeled ~300k documents in minutes versus using manual labeling, all while saving $10M in costs.
1 day
vs. 1 year to adjust label schema
Case Study
Fortune 500 Biotech
A Fortune 500 biotech pioneer leveraged Snorkel Flow to extract critical chronic disease data from clinical trials, accurately processing 300K documents in minutes.
Read More
Problem
Building AI applications to extract entities requires high domain expertise, and large amounts of labeled training data, which is expensive and time consuming.
Solution
Used Snorkel Flow to build a custom model with 99.1% accuracy by adjusting label schema and re-labeling done in hours.
Results
With Snorkel Flow, this biotech giant programmatically labeled ~300k documents in minutes versus using manual labeling, all while saving $10M in costs.
$10M
saved on labeling for extraction
99.1%
accuracy on complex ML pipeline
1 day
vs. 1 year to adjust label schema
Case study
Intel
Intel used Snorkel to replace a high-cost, high-latency crowdsourcing pipeline and accelerate sales and marketing agents.
Read more
Problem
Rapidly changing sales goals make social media monitoring difficult to maintain.
Solution
Deployed a proto version of Snorkel(Snorkel Osprey) to rapidly replace crowdworker labels that took months with programmatically generated labels.
Results
Better performance and major cost savings in Sales & Marketing and Advanced Analytics.
6 months
of crowdworker labels replaced
+18.5
point performance improvement
+28.5
coverage percentage points
Case study
Intel
Intel used Snorkel to replace a high-cost, high-latency crowdsourcing pipeline and accelerate sales and marketing agents.
Read more
Problem
Rapidly changing sales goals make social media monitoring difficult to maintain.
6 months
of crowdworker labels replaced
Solution
Deployed a proto version of Snorkel (Snorkel Osprey) to replace months-long crowdworker labels with inexpensive and fast programmatic labeling.
+18.5
performance improvement
Results
Better performance and major cost savings in Sales and Marketing and Advanced Analytics.
+28.5
coverage percentage points
Case Study
Intel
Intel used Snorkel to replace a high-cost, high-latency crowdsourcing pipeline and accelerate sales and marketing agents.
Learn More
Problem
Rapidly changing sales goals make social media monitoring difficult to maintain.
Solution
Deployed a proto version of Snorkel (Snorkel Osprey) to replace months-long crowdworker labels with cheap & fast programmatic labeling.
Results
Better performance and major cost savings in Sales & Marketing and Advanced Analytics.
6 Months
of crowdworker labels replaced
+18.5
performance improvement
+28.5
coverage percentage points
Case study
Fortune 500 telecom
A Fortune 500 telecom provider used Snorkel Flow to classify encrypted network data flows into their associated application categories.
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Problem
AI-enabled network applications are blocked by the lack of training data, which is typically slow and time-consuming to create and requires network expertise.
Solution
They used Snorkel’s programmatic labeling to precisely classify network traffic, taking advantage of unlabeled/partially labeled data.
Results
The telco trained 200k labels in hours and achieved +25% accuracy above their ground truth baseline using Snorkel Flow’s comprehensive network data exploration and analysis tools.
100k
labels trained in hours
+25%
accuracy above ground truth baseline
+75%
accuracy improved on critical data slice
Case study
Fortune 500 telecom
A Fortune 500 telecom provider used Snorkel Flow to classify encrypted network data flows into their associated application categories.
Read more
Problem
AI-enabled network applications are blocked by the lack of training data, which is typically slow and time-consuming to create and requires network expertise.
100K
labels trained in hours
Solution
They deployed Snorkel’s unique programmatic labeling to precisely classify network traffic, while taking advantage of unlabeled/partially labeled data.
+25%
accuracy above ground truth baseline
Results
The telco trained 200K labels in hours and achieved +25% accuracy above ground truth baseline, all using Snorkel’s comprehensive network data exploration and analysis tools.
+75%
accuracy improved on critical data slice
Case Study
Fortune 500 Telco
A Fortune 500 telecom provider used Snorkel Flow to classify encrypted network data flows into their associated application categories.
Read More
Problem
AI-enabled network applications are blocked by the lack of training data, which is typically slow and time-consuming to create and requires network expertise.
Solution
They deployed Snorkel’s unique programmatic labeling to precisely classify network traffic, while taking advantage of unlabeled/partially labeled data.
Results
The telco trained 200K labels in hours and achieved +25% accuracy above ground truth baseline, all using Snorkel’s comprehensive network data exploration and analysis tools.
100k
labels trained in hours
+25%
accuracy above ground truth baseline
+75%
accuracy improved on critical data slice
Case study
Fortune 50 bank
In just weeks, a Fortune 50 bank achieved a 25+ point performance gain over a black box vendor solution for news analytics application with Snorkel Flow.
Read more
Problem
The bank needed an accurate way to tag companies in unstructured news text, link them to identifiers (e.g., stock tickers), and classify mentions by sentiment and other aspects.
Solution
The bank used Snorkel Flow to develop an AI-powered news analytics application that monitors target companies' press coverage in unstructured data feeds.
Results
With Snorkel Flow, the team achieved a 25+ point performance gain over a legacy vendor system and internal heuristic approaches.
45x
faster compared to hand-labeling
+90
F1 score for news analytics application
+25%
performance gain over black box vendor system
Case study
Fortune 50 Bank
A Fortune 50 bank achieved a 25+ point performance gain over a black box vendor solution for news analytics application with Snorkel Flow- in just a few weeks.
Read more
Problem
The bank needed an accurate way to tag companies in unstructured news text, link them to identifiers (e.g., stock tickers), and classify mentions by sentiment and other aspects.
45x
faster compared to hand-labeling
Solution
The bank used Snorkel Flow to develop an AI-powered news analytics application that monitors target companies' press coverage in unstructured data feeds.
+90
F1 score for news analytics application
Results
With Snorkel Flow, the team achieved a 25+ point performance gain over a legacy vendor system and internal heuristic approaches.
+25%
point performance gain over black box vendor system
Case Study
Fortune 50 Bank
A Fortune 50 bank achieved a 25+ point performance gain over a black box vendor solution for news analytics application with Snorkel Flow- in just a few weeks.
Read More
Problem
The bank needed an accurate way to tag companies in unstructured news text, link them to identifiers (e.g., stock tickers), and classify mentions by sentiment and other aspects.
Solution
The bank used Snorkel Flow to develop an AI-powered news analytics application that monitors target companies' press coverage in unstructured data feeds.
Results
With Snorkel Flow, the team achieved a 25+ point performance gain over a legacy vendor system and internal heuristic approaches.
45x
faster compared to hand-labeling
+90
F1 score for news analytics application
+25%
point performance gain over black box vendor system
Case study
Stanford Medicine
Researchers at Stanford Medicine used Snorkel to label medical imaging and monitoring datasets, replacing person-years of hand-labeling with several hours of using Snorkel.
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Problem
Labeling training data for triaging models takes person-months to person-years of radiologist time.
Solution
Stanford Medicine 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 hospital systems at Stanford and the Department of Veteran Affairs (VA).
8 months
person-months of labeling replaced
94%
ROC AUC performance
50k+
images labeled in minutes
Case study
Stanford Medicine
Researchers at Stanford Medicine used Snorkel to label medical image datasets, replacing person-years of hand-labeling with several hours of using Snorkel.
Read more
Problem
Labeling training data for triaging models takes person-months to person-years of radiologist time.
8 months
person-months of labeling replaced
Solution
We deployed a cross-modal Snorkel pipeline, matching or exceeding the performance of painstakingly gathered manual labels in hours.
94%
ROC AUC performance
Results
Currently being tested for deployment in Stanford and Department of Veteran Affairs (VA) hospital systems.
50K+
images labeled in minutes
Case Study
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.
Read More
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 Veteran Affairs (VA) hospital systems.
8 months
person-months of labeling replaced
94%
ROC AUC performance
50k+
images labeled in minutes
Case study
Global financial services leader
A global financial services leader extracts financial information from PDFs with 99% accuracy in milliseconds using a financial spreading application built with Snorkel Flow.
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Problem
The bank needed to extract structured financial data from balance sheets and income statements (hOCR PDF) from private company financials.
Solution
The bank used Snorkel Flow to develop an AI-powered financial spreading application that parses textual and spatial/visual data features.
Results
With Snorkel Flow, the team achieved superior performance with greater generalizability (2x coverage) compared to a purely rules-based approach.
2x
coverage compared to rules-based approach
99%
extraction accuracy
45x
faster compared to hand-labeling
Case Study
Global Financial Services Leader
A global financial services leader extracts financial information from PDFs with 99% accuracy in milliseconds using a financial spreading application built with Snorkel Flow.
Read more
Problem
The bank needed to extract structured financial data from balance sheets and income statements (hOCR PDF) from private company financials.
2x
coverage compared to rules-
based approach
Solution
The bank used Snorkel Flow to develop an AI-powered financial spreading application that parses textual and spatial/visual data features.
99%
extraction accuracy
Results
With Snorkel Flow, the team achieved superior performance with greater generalizability (2x coverage) compared to a purely rules-based approach.
45x
faster compared to hand-labeling
Case Study
Global Financial Services Leader
A global financial services leader extracts financial information from PDFs with 99% accuracy in milliseconds using a financial spreading application built with Snorkel Flow.
Read More
Problem
The bank needed to extract structured financial data from balance sheets and income statements (hOCR PDF) from private company financials.
Solution
The bank used Snorkel Flow to develop an AI-powered financial spreading application that parses textual and spatial/visual data features.
Results
With Snorkel Flow, the team achieved superior performance with greater generalizability (2x coverage) compared to a purely rules-based approach.
2x
coverage compared to rules-based approach
99%
extraction accuracy
45x
faster compared to hand-labeling
Case study
Tide
A UK based fintech company used Snorkel to match receivable invoices from the mobile app with incoming transactions.
Read more
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.
15
days to create training dataset and deploy model
97%
ML model accuracy
5M
invoices processed
Case Study
Tide
A UK based fintech company, used Snorkel to match receivable invoices from the mobile app with incoming transactions.
Read more
Problem
Tide needed to label matching invoices with transactions that required investing highly paid subject matter experts’ time in hand-labeling historical data.
15
days to create training dataset & deploy model
Solution
Used Snorkel to programmatically label data, extract information, and harness business knowledge by creating labeling functions.
97%
ML model accuracy
Results
Achieved 97.6% accuracy to detect transactions made for a particular invoice. Created training data programmatically replacing 1000 hours of hand-labeling.
5M
invoices processed
Case Study
Tide
A UK based fintech company, used Snorkel to match receivable invoices from the mobile app with incoming transactions.
Read More
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.
15
days to create training dataset & deploy model
97%
ML model accuracy
5M
invoices processed
