Fully interoperable
Snorkel Flow is an integration-first platform that works
with your existing ML stack seamlessly and securely. Build a data-centric workflow over your infrastructure, data sources, modeling libraries, serving environments, and more.
Python SDK
Pull in existing models, create custom labeling functions, and integrate with your existing data sources, applications, and MLOps infrastructure.
Use Snorkel Flow’s built-in notebooks, or install the Python SDK in other environments to build integrations.
The Snorkel Success Program
The AI/ML expertise of our success managers, engineers, and researchers is unparalleled.
We’ll work with you to enable your team and apply customer-proven best practices, maximizing your ROI and time to value.
Secure and compliant
We’re committed to upholding the security of our customers’ data, and Snorkel Flow is built around these principles. Please email security@snorkel.ai with any questions you may have.
Holistic security investments
Regular third-party penetration testing
Continuous vulnerability scanning
Secure software development and employee training
Data access and protection
Multi-provider single sign-on (SSO)
Native role-based access controls
Encryption in transit (TLS) and at rest (AES 256)
SOC2 Type II Compliant
Snorkel AI’s SOC 2 Type II report covers the trust services categories of security, confidentiality, and availability and is audited annually. The report is available, upon request, for review by existing customers and new prospects.
HIPAA Compliant
Snorkel AI is in compliance with the U.S. Health Insurance and Accountability Act (HIPAA). HIPAA requires any organization who service healthcare clients to comply with regulatory standards governing the security, privacy, and integrity of sensitive health care data, called Protected Health Information (PHI).
Bring everyone’s best to the table
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.
User-tailored workflows
Support for all teammates with both a comprehensive Python SDK and no-code interfaces.
Efficient troubleshooting
Pinpoint data slices for domain expert spot-checks and troubleshooting to improve model accuracy faster.
Rich knowledge transfer
Gather context and insight beyond labels with patterns, tags, and comments.
Learn more
Are you ready to dive in?
Label data programmatically, train models efficiently, improve performance iteratively, and deploy applications rapidly—all in one platform.
Request a demo