Snorkel Flow - Build specialized AI with your data and expertise

Snorkel Flow Build specialized AI with your data and expertise—100x faster

What is Snorkel Flow?

Snorkel Flow is a collaborative platform designed for data scientists and subject matter experts. It enables the capture of domain knowledge and its application to entire datasets, significantly accelerating the data labeling process. The platform supports programmatic labeling, annotation, and development of data, changing the way these professionals work and eliminating the need for manual data handling. Snorkel Flow facilitates the quick iteration of training data and model development through integrated, guided error analysis and model evaluation.

Snorkel Flow ensures seamless interoperability with existing AI/ML stacks, so users can continue utilizing familiar technologies. It's trusted by world-leading data science teams and supports LLM fine-tuning, RAG optimization, and domain-specific LLM evaluation, ensuring AI applications meet high accuracy, ethical standards, company policies, and industry regulations.

Features

Programmatic Data Labeling: Capture domain knowledge and apply it to entire datasets, eliminating manual labeling.
LLM Fine-Tuning: Accelerate the curation of high-quality training data for specialized LLMs.
RAG Optimization: Improve retrieval accuracy by optimizing document metadata, chunking, and embedding models.
LLM Evaluation: Create domain-specific LLM evaluations beyond standard benchmarks.
Guided Error Analysis: Built-in tools for analyzing errors and improving model performance.
Collaborative Platform: Facilitates teamwork between data scientists and subject matter experts.

Use Cases

Developing specialized AI applications for classification and information extraction.
Transforming images into insights with computer vision.
Creating domain-specific LLM evaluations for unique business policies.
Labeling and annotating large datasets rapidly.
Fine-tuning LLMs to meet production accuracy requirements.
Improving RAG retrieval accuracy for better LLM responses.
Building NLP applications for named entity recognition.