Label & Build
Label and build training data programmatically in hours without months of hand-labeling
Integrate & Manage
Automatically clean, integrate, and manage programmatic training data from all sources
Train & Deploy
Train and deploy state-of-the-art machine learning models in-platform or via Python SDK
Analyze & Monitor
Analyze and monitor model performance to rapidly identify and correct error modes in the data
1
Label —
Label, augment, and build training data programmatically
Snorkel Flow starts with a fundamental departure from the status quo of labeling by hand: users develop programmatic operators to label, augment, and build training data. These range from simple but powerful push-button labeling functions — e.g., expressing rules or heuristics — to complex custom operators bringing diverse sources of signal to bear via the Python SDK. The result is a radically faster, more flexible interface to AI.
2
Manage —
Clean, integrate, and manage data for AI
Snorkel Flow automatically cleans, integrates, and manages the programmatic operators developed by users, relying on theoretically-grounded algorithmic techniques. Snorkel Flow also integrates hand-labeled data or other sources of signal to produce high-quality, versioned, and auditable training data: the high-octane fuel for AI.
3
Train and Deploy —
Train and deploy state-of-the-art models
In Snorkel Flow, rather than waiting on data to be labeled by hand, users can train and deploy state-of-the-art ML models with a button push, or use a Python SDK to plug into their own modeling pipelines. The result is high-accuracy models and applications beyond the speed of hand-labeled data.
4
Analyze and Monitor —
Analyze and monitor to close the loop
In Snorkel Flow, monitoring and analysis enables users to rapidly identify and correct error modes in their data and models. The net effect: AI as an iterative development process, rather than a one-and-done exercise bound by the data.
Case Study: Proven Technology
Snorkel Flow is based on technology developed at the Stanford AI Lab, and deployed with some of the world's leading organizations. For example, the Snorkel AI team deployed an early version of the Snorkel Flow technology with teams at Google, finding that it could replace hand-labeling hundreds of thousands of data points and offer increased flexibility and speed.
10-0K+
Hand labels replaced
0%
Improvement by repurposing organizational resources
0M+
Labels in < 30 min.
Application deployment —
Build end-to-end AI application workflows that go beyond single models
Build full, AI-powered application workflows — from data ingestion and preprocessing to postprocessing and business process automation.
Deploy end-to-end workflows developed in Snorkel Flow — including custom code operators — as REST APIs with the push of a button to power your applications.
With flexible on-premises and self-hosted installation options, Snorkel Flow's programmatic labeling powers the first truly privacy-first ML solution.
Advanced developer access —
Configurable and interoperable via Python SDK
Use Snorkel Flow's Python SDK to drive development workflows from data processing to model training to deployment — enabling programmatic control and easy interoperability.
Team collaboration —
A collaborative platform for the cross-functional teams that drive AI
Successful AI solutions require collaboration across roles and teams — from the subject matter expert's knowledge to the ML engineer's deployment pipeline, Snorkel Flow is the only end-to-end platform that truly brings all roles together via the data.