How Snorkel Flow works —
Powered By Programmatic Labeling
Label data programmatically, train models efficiently, improve performance iteratively, and deploy applications rapidly—all in one platform.
01 Label & Build
Rather than hand-labeling thousands of data points by hand, use Data Studio to programmatically label massive amounts of training data using labeling functions—rules, heuristics, and other custom complex operators—via a push-button UI or Python SDK using integrated notebooks. Get started quickly with ready-made labeling functions (LF) builders, data exploration tools, or nifty auto-suggest features. Receive instant feedback with coverage and accuracy estimates of your LFs to develop a high-quality training data set.
Ready-made Labeling Function Builders
02 Integrate & Manage
Snorkel Flow automatically learns the different labeling functions’ accuracies, denoises and integrates them, and stores versioned LF packages and training data in Data Manager. Unlike with hand-labeled data, you create training data in Snorkel Flow using code, so you can audit, modify, or serve it almost instantly. Snorkel Flow makes it easy to share resources, both LF and training data, with others on the team--no need to reinvent the wheel.
03 Train & Deploy
Train state-of-the-art ML models with a button push or via Python SDK using integrated notebooks to plug into your existing modeling pipelines. Snorkel Flow provides access to popular open-source model libraries that you can train on CPU- or GPU-based computing infrastructure. Snorkel Flow makes it easy to tune your models with automated hyperparameter search. Deploy these high-accuracy models immediately as real-time or batch APIs or via the SDK.
Snorkel Flow Model Zoo
04 Analyze & Monitor
Snorkel Flow includes several commonly used and custom analysis tools to compare multiple models over different data splits. It offers suggestions on improving model quality by adding or editing LFs or optimizing the model to target specific errors. You can also monitor performance drifts in LFs or the model and rapidly adapt to changes without relabeling from scratch. The result: AI development is now an iterative process rather than a one-and-done exercise that leaves performance on the table.
Watch How Snorkel Flow Works
Data-First AI Development
Rather than relying solely on generic third-party models, brittle rule-based systems, or armies of human labelers, accelerate AI development with a new, data-first approach using Snorkel Flow.
With Snorkel Flow
With Conventional Approaches
Advanced features to foster collaboration across roles, from data scientists and developers to subject matter experts, and leverage data at enterprise scale to build highly-accurate models.
Application Studio is a visual builder with pre-built solutions for industry-specific use cases and common AI tasks, giving you a head start developing ML-based applications over your data. Packaged application-specific pre- and post-processors, labeling functions, models, as well as a library of operator DAGs make customizing applications as easy as dragging and dropping new operators into the application flow.
*Application Studio is in preview and will be generally available later in 2021.
Easy Integrations & Interoperability
Integrating Snorkel Flow with other machine learning and data systems is as easy as writing a line of Python–quickly integrate your existing training labels, data, models, and full applications with Snorkel Flow’s SDK and API access points at all stages in the development/deployment pipeline.
Diverse Data Types
Snorkel’s technology has been proven to work with a wide range of data types and the use of cross-modal data--enabling solutions for use cases that weren’t possible before.
Flexible UI for All Teams
Snorkel Flow’s no-code GUI, Python SDK, and developer APIs are fully-interoperable, so your entire team—from engineers and data scientists to subject matter experts—can collaborate in the development workflow.
Secure, Scalable Deployment
Range of enterprise-grade secure deployment options available including single-node or distributed on Kubernetes based on your needs.
SECURE PRIVATE CLOUD/ON-PREM
SECURE PUBLIC CLOUD
SECURE SNORKEL CLOUD
User community —
What the Data Scientists Are Saying
JASON ALAN FRIES
Director, Recommendation Systems
AI Use Cases
Build and deploy use cases previously blocked by training data by combining state-of-the-art ML with industry-specific best practices and flexible API-based integrations using Snorkel Flow.
Classify policy documents on the basis of the behavior or occupation to assess risk.
Build customized promotions by analyzing customer behavior and demographics.
Determine clinical trial candidates by categorizing patient records.
Extract entities, events, and relationships to improve investment and risk strategies and more.
TELECOM & CYBER
Understand every customer interaction deeply by analyzing chats, emails, and tickets.
Manage credit risk by collecting financial and non-financial data in any format from statements.
Identify named entities in customer search queries and optimize content on websites.
Enhance recommender systems by identifying entities (price, keywords, etc.) in product descriptions.
Extract and organize data from a wide variety of complex contracts efficiently.