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How data slices transform enterprise LLM evaluation
Enterprises must evaluate LLM performance for production deployment. Custom, automated eval + data slices present the best path to production.
Our picks
Call center AI for customer experience management: a case study
How one large financial institution used call center AI to inform customer experience management with real-time data.
RAG: Boost LLM performance with retrieval-augmented generation
Retrieval-augmented generation (RAG) enables LLMs to produce more accurate responses by finding and injecting relevant context. Learn how.
Join AI experts, researchers, and data scientist who are at the forefront of AI.
Latest posts
How a global financial services company built a specialized AI copilot accurate enough for production
Learn how Snorkel, Databricks, and AWS enabled the team to build and deploy small, specialized, and highly accurate models which met their AI production requirements and strategic goals.
Task Me Anything: innovating multimodal model benchmarks
“Task Me Anything” empowers data scientists to generate bespoke benchmarks to assess and choose the right multimodal model for their needs.
Alfred: Data labeling with foundation models and weak supervision
Introducing Alfred: an open-source tool for combining foundation models with weak supervision for faster development of academic data sets.
RAG: Boost LLM performance with retrieval-augmented generation
Retrieval-augmented generation (RAG) enables LLMs to produce more accurate responses by finding and injecting relevant context. Learn how.
Latest videos
Knowledge Distillation Demystified: Techniques and Applications
Model Compression: Streamlining AI for Enterprises
Model Distillation: From Large Models to Efficient Enterprise Solutions
How to Evaluate LLM Performance for Domain-Specific Use Cases
Understand the basics of LLM training in under four minutes!
RAG Optimization: A Practical Overview for Improving Retrieval Augmented Generation
Applied AI
RAG: Boost LLM performance with retrieval-augmented generation
Retrieval-augmented generation (RAG) enables LLMs to produce more accurate responses by finding and injecting relevant context. Learn how.
Snorkel AI signs strategic collaboration agreement with AWS to help enterprises cross the demo-to-production chasm
To tackle generative AI use cases, Snorkel AI + AWS launched an accelerator program to address the biggest blocker: unstructured data.
AI alignment made simple: innovative solutions for businesses
AI alignment ensures that AI systems align with human values, ethics, and policies. Here’s a primer on how developers can build safer AI.
Walking safely before building flying saucer seatbelts: introducing Enterprise Alignment
Snorkel takes a step on the path to enterprise superalignment with new data development workflows for enterprise alignment
Customers
Call center AI for customer experience management: a case study
How one large financial institution used call center AI to inform customer experience management with real-time data.
How we achieved 89% accuracy on contract question answering
A customer wanted an llm system for complex contract question answering tasks. We helped them build it—beating the baseline by 64 points.
Content filtering breakthrough: Snorkel client reaches 96% recall in 3 days
Snorkel AI helped a client solve the challenge of social media content filtering quickly and sustainably. Here’s how.
First cohort of Snorkel GenAI customers sees gains up to 54 points
In its first six months, Snorkel Foundry collaborated on high-value projects with notable companies and produced impressive results.
Data development
How a global financial services company built a specialized AI copilot accurate enough for production
Learn how Snorkel, Databricks, and AWS enabled the team to build and deploy small, specialized, and highly accurate models which met their AI production requirements and strategic goals.
Task Me Anything: innovating multimodal model benchmarks
“Task Me Anything” empowers data scientists to generate bespoke benchmarks to assess and choose the right multimodal model for their needs.
Alfred: Data labeling with foundation models and weak supervision
Introducing Alfred: an open-source tool for combining foundation models with weak supervision for faster development of academic data sets.
New GenAI features, data annotation: Snorkel Flow 2024.R2
This release features new GenAI tools and Multi-Schema Annotation, as well as new enterprise security tools and an updated home page.
Product
How does the Snorkel Flow label model work?
The Snorkel Flow label model plays an instrumental role in driving the enterprise value we create. Here’s a peek at how it works.
How to build production-grade RAG retrieval with Snorkel Flow
See a walkthrough of how Snorkel Flow users build applications with production-grade RAG retrieval components.
Role-based access controls in Snorkel Flow secure enterprise data
Snorkel Flow’s 2024.R1 release includes new role-based access control tools to further safeguard valuable enterprise data.
Discover what’s new in Snorkel Flow: Flexible data and LLM connectivity, secure data controls, and more!
Unlock advanced LLM customization with Snorkel Flow’s new release! Explore flexible data integrations, secure controls, and multimodal support to fine-tune language models for enterprise use. Discover how to leverage images and diverse data types for AI-driven insights.
Research
Long context models in the enterprise: benchmarks and beyond
Snorkel researchers devised a new way to evaluate long context models and address their “lost-in-the-middle” challenges with mediod voting.
How ROBOSHOT boosts zero-shot foundation model performance
ROBOSHOT acts like a lens on foundation models and improves their zero-shot performance without additional fine-tuning.
Snorkel teams with Microsoft to showcase new AI research at NVIDIA GTC
Microsoft infrastructure facilitates Snorkel AI research experiments, including our recent high rank on the AlpacaEval 2.0 LLM leaderboard.
How Skill-it! enables faster, better LLM training
Humans learn tasks better when taught in a logical order. So do LLMs. Researchers developed a way to exploit this tendency called “Skill-it!”
Trending stories
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Fine-tuning specialized LLMs demands a lot of time and cost We developed Bonito to make this process faster, cheaper, and easier.
2
The founding team of Snorkel AI has spent over half a decade—first at the Stanford AI Lab and now at Snorkel AI—researching data-centric techniques to overcome the biggest bottleneck in AI: The lack of labeled training data. In this video Snorkel AI co-founder Paroma Varma gives an overview of the key principles of data-centric AI development. What is data-centric AI?…
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We need more labeled data than ever, so we have explored weak supervision for non-categorical applications—with notable results.
4
Large language models have enormous potential. But what are they? Where did they come from? And how can you make them work better?
5
Snorkel takes a step on the path to enterprise superalignment with new data development workflows for enterprise alignment
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The founding team of Snorkel AI has spent over half a decade—first at the Stanford AI Lab and now at Snorkel AI—researching programmatic labeling and other techniques for breaking through the biggest bottleneck in AI: the lack of labeled training data. This research has resulted in the Snorkel research project and 150+ peer-reviewed publications. Snorkel’s programmatic labeling technology has been…
Latest posts
How a global financial services company built a specialized AI copilot accurate enough for production
Learn how Snorkel, Databricks, and AWS enabled the team to build and deploy small, specialized, and highly accurate models which met their AI production requirements and strategic goals.
Task Me Anything: innovating multimodal model benchmarks
“Task Me Anything” empowers data scientists to generate bespoke benchmarks to assess and choose the right multimodal model for their needs.
Alfred: Data labeling with foundation models and weak supervision
Introducing Alfred: an open-source tool for combining foundation models with weak supervision for faster development of academic data sets.
RAG: Boost LLM performance with retrieval-augmented generation
Retrieval-augmented generation (RAG) enables LLMs to produce more accurate responses by finding and injecting relevant context. Learn how.
Call center AI for customer experience management: a case study
How one large financial institution used call center AI to inform customer experience management with real-time data.
New GenAI features, data annotation: Snorkel Flow 2024.R2
This release features new GenAI tools and Multi-Schema Annotation, as well as new enterprise security tools and an updated home page.
How data slices transform enterprise LLM evaluation
Enterprises must evaluate LLM performance for production deployment. Custom, automated eval + data slices present the best path to production.
Meta’s Llama 3.1 405B is the new Mr. Miyagi, now what?
Meta’s Llama 3.1 405B, rivals GPT-4o in benchmarks, offering powerful AI capabilities. Despite high costs, it can enhance LLM adoption through fine-tuning, distillation, and as an AI judge.
Meta’s new Llama 3.1 models are here! Are you ready for it?
Meta released Llama 3 405B today, signaling a new era of open source AI. The model is ready to use on Snorkel Flow.
Data-centric AI with Snorkel and MinIO
High-performing AI systems require more than a well-designed model. They also require properly constructed training and testing data.
Weak supervision for non-categorical applications + superalignment
We need more labeled data than ever, so we have explored weak supervision for non-categorical applications—with notable results.
Snorkel AI signs strategic collaboration agreement with AWS to help enterprises cross the demo-to-production chasm
To tackle generative AI use cases, Snorkel AI + AWS launched an accelerator program to address the biggest blocker: unstructured data.
AI alignment made simple: innovative solutions for businesses
AI alignment ensures that AI systems align with human values, ethics, and policies. Here’s a primer on how developers can build safer AI.
How does the Snorkel Flow label model work?
The Snorkel Flow label model plays an instrumental role in driving the enterprise value we create. Here’s a peek at how it works.
Vision language models: how LLMs boost image classification
Vision language models demonstrate impressive image classification capabilities, but LLMs can help improve their performance. Learn how.
Long context models in the enterprise: benchmarks and beyond
Snorkel researchers devised a new way to evaluate long context models and address their “lost-in-the-middle” challenges with mediod voting.
How to build production-grade RAG retrieval with Snorkel Flow
See a walkthrough of how Snorkel Flow users build applications with production-grade RAG retrieval components.
How Bonito helps fine-tune specialized LLMs faster than ever
Fine-tuning specialized LLMs demands a lot of time and cost We developed Bonito to make this process faster, cheaper, and easier.
Walking safely before building flying saucer seatbelts: introducing Enterprise Alignment
Snorkel takes a step on the path to enterprise superalignment with new data development workflows for enterprise alignment
Role-based access controls in Snorkel Flow secure enterprise data
Snorkel Flow’s 2024.R1 release includes new role-based access control tools to further safeguard valuable enterprise data.
Accelerating AI development in manufacturing with Snorkel Flow and AWS SageMaker
The manufacturing industry has experienced a massive influx of data. Snorkel AI and AWS Sage Maker can make that data actionable.
How ROBOSHOT boosts zero-shot foundation model performance
ROBOSHOT acts like a lens on foundation models and improves their zero-shot performance without additional fine-tuning.
Discover what’s new in Snorkel Flow: Flexible data and LLM connectivity, secure data controls, and more!
Unlock advanced LLM customization with Snorkel Flow’s new release! Explore flexible data integrations, secure controls, and multimodal support to fine-tune language models for enterprise use. Discover how to leverage images and diverse data types for AI-driven insights.
Faster than ever document intelligence with new Snorkel Flow FM-first workflow
Snorkel Flow’s new FM-first workflow for building document intelligence applications will get you from demo to production faster than ever.
The art of data development for Enterprise LLMs
Snorkel’s Paroma Varma and Google’s Ali Arsenjani discus the role of data in the development and implementation of LLMs.
Crossing the demo-to-production chasm with Snorkel Custom
We’re excited to announce Snorkel Custom to help enterprises cross the chasm from flashy chatbot demos to real production AI value.
How Snorkel topped the AlpacaEval leaderboard (and why we’re not there anymore)
Snorkel AI placed a model at the top of the AlpacaEval leaderboard. Here’s how we built it, and how it changed AlpacaEval’s metrics.
CRFM’s HELM and enterprise LLM evaluation beyond accuracy
As Snorkel AI prepares to build better enterprise LLM evaluations, we spoke with Yifan Mail from Stanford’s CRFM HELM project.
How we achieved 89% accuracy on contract question answering
A customer wanted an llm system for complex contract question answering tasks. We helped them build it—beating the baseline by 64 points.
Five sessions not to miss at Google Cloud Next 24
Snorkel AI will be at Google Cloud Next. The event will feature more than 700 sessions, so we picked five that we think you shouldn’t miss.
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