AI for telecom

From customer service operations to network health and security, Snorkel Flow provides telecom innovators with a data-centric platform to build custom AI applications powered by programmatic data labeling.

Request a demo

AI For Telecom

From customer service operations to network health and security, Snorkel Flow provides telecom innovators with a data-centric platform to build custom AI applications powered by programmatic data labeling.

Request a demo



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.

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 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.

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

Data-centric AI

Snorkel AI is leading the shift from model-centric
to data-centric AI development to make AI practical.
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Accelerated

Save time and costs by replacing manual labeling with rapid, programmatic labeling.
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Adaptable

Adapt to changing data or business goals by quickly changing code, not manually re-labeling entire datasets.
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Collaborative

Incorporate subject matter experts' knowledge by collaborating around a common interface–the data needed to train models.
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Accurate

Develop and deploy high-quality AI models via rapid, guided iteration on the part that matters–the training data.
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Governable

Version and audit data like code, leading to more responsive and ethical deployments.
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Private

Reduce risk and meet compliance by labeling programmatically and keeping data in-house, not shipping to external annotators.

Use cases

AI solutions for telecom

AI applications built using Snorkel Flow make the most of data resources by integrating them with cutting-edge models and expertise. Automate manual, legacy labeling processes, and reserve expensive human-powered work for where it’s needed.
Intrusion detection
Detect anomalous internet traffic and respond to stop malicious activity.
Personalization
Build personalized promotions and products by analyzing customer behavior and segmentation.
Network optimization
Monitor network performance, detect and predict issues with precision. Efficiently reallocate resources in real-time.
Geospatial analysis
Tie information from documents to geospatial analysis to discover new market opportunities.

A radically new approach to AI

Conventional AI approaches rely on generic third-party models, or brittle rule-based systems, or armies of human labelers. With Snorkel Flow, programmatically labeling unlocks a new workflow that accelerates AI app development.

With Snorkel Flow

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Customize state-of-the-art models by training with your data & adapt to changing data or goals with a few lines of code.
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Leverage cutting-edge ML to go beyond simple rules and retain the flexibility to audit and adapt.
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Label thousands of data points programmatically in hours while keeping your data in-house and private.

With conventional approaches

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Hand-labeled ML is hugely expensive, with usually no way to iterate, adapt, be privacy compliant, audit, or reuse.
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Pre-trained vendor models often don’t work on your data, no way to customize, adapt, or audit.
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Rules-based approaches often don’t perform well on complex data or adapt easily to data or goal changes.

The platform for data-centric AI development

The platform for data-centric AI development

Dive in

[get_press_posts]
Press
Blog
Research
Case studies
Press
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November 17, 2022
Snorkel AI Accelerates Foundation Model Adoption with Data-centric AI


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November 17, 2022
AI startup Snorkel preps a new kind of expert for enterprise AI


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November 17, 2022
Snorkel dives into data labeling and foundation AI models


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July 28, 2022
Here’s why a gold rush of NLP startups is about to arrive


Blog
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November 17, 2022
Data-centric Foundation Model Development: Bridging the gap between foundation models and enterprise AI


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November 17, 2022
Better not bigger: How to get GPT-3 quality at 0.1% the cost


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November 3, 2022
Building an NLP application to analyze ESG factors in Earnings Calls using Snorkel Flow


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August 4, 2022
The Future of Data-Centric AI 2022 day 1 highlights


Research
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2022
Universalizing Weak Supervision


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2021
Ontology-driven weak supervision for clinical entity classification in electronic health records


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2017
Rapid Training Data Creation with Weak Supervision


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2016
Data Programming: Creating Large Datasets Quickly


Customer Stories
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September 30, 2022
How Schlumberger uses Snorkel Flow to enhance proactive well management


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September 30, 2022
How a global custodial bank automated KYC verification with Snorkel Flow


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September 28, 2022
How Memorial Sloan Kettering Cancer Center used Snorkel Flow to scale clinical trial screening


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February 26, 2022
How Genentech extracted information for clinical trial analytics with Snorkel Flow


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Are you ready to dive in?

Label data programmatically, train models efficiently, improve performance iteratively, and deploy applications rapidly—all in one platform.
Request a demo