Snorkel AI Ushers in Support for Specialized, Highly Accurate AI Models

Snorkel AI, the company making enterprise AI data development programmatic, is debuting new specialization capabilities for Snorkel Flow, the AI data development platform. Designed to scale AI development practices and accelerate specialized AI/ML models, Snorkel Flow continues to support the entire AI data development lifecycle, according to the company.

Snorkel’s latest release was prompted by the need for AI-ready data, which is defined by Gartner as, “representative of the use case, of every pattern, error, outlier, and unexpected emergence that is needed to train or run the AI model for the specific use. Data readiness for AI is not something you can build once and for all, nor that you can build ahead of time for all your data.”

To ensure enterprises can apply scalable AI data development practices, as well as accelerate the production delivery of accurate, specialized models, Snorkel Flow now offers the following capabilities:

• Customizable LLM evaluation tools for domain-specific use cases that offer insight into error modes, further accompanied by custom acceptance criteria that evaluates large language model (LLM) performance for specific use cases as well as if additional training is required
• Retrieval-augmented generation (RAG) tuning workflows that improve retrieval accuracy through advanced chunking, fine-tuning embedding models, and document metadata extraction, ultimately reducing development time for improving AI quality
• Named Entity Recognition (NER) for PDFs which quickly extracts information from PDFs with ease, enabling users to click on words, draw bounding boxes, specify patterns, and prompt foundation models
• Simplified annotation and feedback, streamlining the interface for multi-criteria evaluation and enabling users to annotate data and provide feedback simultaneously
• User experience enhancements that standardizes collaboration on the platform and streamlines navigation, workflows, and data labeling, encouraging subject matter experts (SMEs) and data scientists to work together with ease
• AI ecosystem integration with leading AI development platforms—such as Databricks Data Intelligence Platform and Amazon SageMaker—to fine tune and deploy specialized models, accompanied by expanded support for LLM prompting with popular foundation models, including OpenAI ChatGPT, Google Gemini, and Meta Llama

“AI is at the top of every enterprise leader’s priority list, but the deep work needed for consistent, repeatable AI development is daunting, costly, and manual,” said Alex Ratner, CEO and co-founder of Snorkel AI. “Data underpins successful adoption of AI in large enterprises, which is why these updates to our AI data development platform are so important. They’re fundamental to helping enterprises accelerate and optimize the delivery of AI solutions.”

To learn more about Snorkel AI, please visit https://snorkel.ai/.