AI data development
The only way to rely on your models is if you can trust the data that built them
Programmatic data development
Maximize your data's potential through programmatic AI data development, creating real-world business value with software-like familiarity.
- Precisely curate data at scale with programmatic data operations to efficiently label, filter, slice, sample, and augment your data.
- Trustworthy system of record that builds transparent and auditable training datasets with the power of Labeling Functions (LFs).
- Leverage unlimited flexibility when creating or updating your LFs to meet changing business requirements and generate new training data.
- Use advanced prompting with the latest LLMs to label your data across multiple modalities.
Guided error analysis
Automatically detect ways to improve your data quality and correct errors based on inputs from your models and SMEs.
- Optimize your data tuning with your choice of preprocessors that match your data modality, labeling schemas, and error corrections.
- Simplify your data exploration process with our user-friendly UI or SDK to effortlessly explore raw data and improve model performance.
- Get instant feedback on the coverage and accuracy of your data operations through automated error correlation.
- Rely on model-driven suggestions to resolve unique errors and edge cases based on integrated model training.
Unified prompting, RAG, and fine-tuning
Easily integrate prompting and programmatic labeling as part of a unified development journey.
- Single auditable platform to maintain all your model fine-tuning training data for instruction tuning and correction, RAG embeddings, and more.
- Write a prompt in natural language to curate data, include RAG connectors for additional context, and get instant feedback on performance.
- Quickly Iterate on prompts and write labeling functions, incorporating SME feedback to correct errors, then fine-tune or distill a smaller, more accurate model.
Subject matter expert collaboration
Enhance the productivity of your Subject Matter Experts (SMEs) and annotation teams with direct and seamless collaboration between business and data science teams.
- Simplify your workflow with our fully integrated annotator suite's low-code and no-code solutions, crafted for domain experts to easily label, annotate, and resolve annotation tasks.
- Facilitate a human-in-the-loop feedback cycle that enables agile iteration and adaptation of data and models, ensuring improved and reliable model performance in production.
- Optimize team coordination with seamless annotator management, ensuring tasks are assigned, guidelines are set, and disagreements are resolved quickly and accurately.
Ready for production
Fortune 500 enterprises trust Snorkel Flow for its reliability, governability, security, and compliance with industry standards.
- High-quality curated data to train models in your infrastructure or use enriched data to improve RAG.
- Fine-tuned LLMs< that use curated data from Snorkel Flow to fine-tune the latest LLMs.
- Distilled specialized models with a 10,000-fold size reduction and similar or higher accuracy as base LLMs.
- Gain peace of mind knowing you're supported by world-class ML experts and decades of Stanford AI lab research on a platform that's trusted industry-wide.
Interoperable with your AI stack
Data ingest
Quickly and securely integrate to data pipelines or upload data locally.
Model training
Train custom models or choose from leading model frameworks with optional AutoML.
Production serving
Deploy your models within Snorkel Flow or export to the service of your choice.
Infrastructure
Host Snorkel Flow within the secure infrastructure of your choice.
Power high-value use cases
Computer Vision
Generative AI
Let's make it real.
Set a new pace for AI
Schlumberger built an AI app in 3 days
Built an AI application in 3 days that reduced time to extract information from oil well drilling reports from 1 to 3 hours per report to just a few seconds.
Google improved accuracy by 52%
Genentech saved over $10M
Additional platform benefits
Privacy-safe
Explainable
Reusable
Collaborative
