Programmatic labeling accelerates training data creation 10-100x.
Deliver high-quality training data that’s more explainable and adaptable in minutes, not months.
All knowledge, from expert heuristics to foundation model insights, provides valuable labeling signal.
These inputs, which can be imprecise and conflict, are intelligently combined and applied at scale.
Real-time model training and analysis shows the quality of both labeling functions and data.
Guided iteration shows you where—and how—to improve, including many automated 1-click actions.
Capture labeling signal from diverse sources
Write labeling functions in the no-code UI or using the SDK to capture heuristics and resources across a range of complexity. Snorkel Flow combines and refines them to label at scale.
Native support for complex data types
Speed development even when data is messy, unstructured, or highly variable. Use pre- and post-processors, data viewers, and LF types purpose-built for text, PDFs, conversational data, and more.
Semi-structured/ tabular data
Adapt easily to inevitable changes
Keep models performant in the face of data drift or objective changes with seamless label schema and labeling function updates. Snorkel Flow regenerates your entire training set so you’re ready to retrain your model in minutes.
Elevate collaboration with
You rely on your domain experts and business partners for insight, expertise, and feedback. Snorkel Flow makes it easy to transfer knowledge, not just labels.
Real-time progress sharing
Work in a single platform to remove the silos between domain experts, annotators, and data scientists.
Support for all teammates with both a comprehensive Python SDK and no-code interfaces.
Pinpoint data slices for domain expert spot-checks and troubleshooting to improve accuracy faster.
Rich knowledge transfer
Encode the rationale behind labeling decisions with labeling functions which are inspectable, adaptable, and reusable.
Platform labeling capabilities
Flexible labeling function creation
UI-based, custom code, and auto-suggested labeling functions to capture diverse sources of input.
Snorkel Flow’s label models aggregate your labeling functions intelligently to produce training labels en masse.
Labeling functions from external models
Incorporate signal from integrated state-of-the-art foundation models with natural language prompts.
Use model guidance to prioritize programmatic labeling effort against the highest-impact slices of data.
Labeling functions from existing labels
Reuse existing labels (even noisy ones) as labeling functions that are combined and corrected by other labeling sources.
Interface for domain experts to label ground truth and troubleshoot challenging slices during iteration.
Get real-time quantitative and qualitative feedback on the labeling functions you write for guided iteration.
Diverse data displays
Multiple ways to view your data (individual, table, clusters, etc.) help you understand and label it more efficiently.