author
Co-founder
,
Snorkel AI
Henry Ehrenberg is a co-founder of Snorkel AI, focused on technical strategy and engineering. He has been a core Snorkel team member since the project’s origins in the Stanford AI Lab, building the open-source research library and conducting research on programmatic data labeling and augmentation.
Before Snorkel AI, Henry was the tech lead for Facebook Applied AI’s representation learning team. Henry earned his master’s degree in computational and mathematical engineering from Stanford University, and his bachelor’s degree in applied mathematics from Yale University.
The latest from Henry
Blog
Snorkel AI Teams with Google Cloud and Vertex AI to speed AI deployment
Snorkel AI, Google Cloud and Vertex AI partner to help organizations transform data into AI-powered systems faster than ever.
Blog
Deepening Snorkel AI’s partnership with Microsoft Azure AI
Snorkel AI is excited to build on our partnership with Microsoft Azure to help enterprises and government agencies solve their most impactful problems and unlock value from their data using AI. Learn how Azure customers can easily deploy Snorkel Flow on their Azure cloud infrastructure to accelerate AI application development with data-centric workflows and programmatic labeling.
Blog
Q4 LTS Release of Snorkel Flow
We’re excited to announce the Q4 2021 LTS release of Snorkel Flow, our data-centric AI development platform powered by programmatic labeling. This latest release introduces a number of new product capabilities and enhancements, from a streamlined programmatic data development interface, to enhanced auto-suggest for labeling functions, to new machine learning capabilities like AutoML, to significant performance enhancements for PDF data…
Snorkel: Fast Training Set Generation for Information Extraction
Introducing Snorkel, a new system for quickly creating, managing, and modeling training datasets.
Research Paper
Snorkel: Fast Training Set Generation for Information Extraction
Introducing Snorkel, a new system for quickly creating, managing, and modeling training datasets.
Learning to Compose Domain-Specific Transformations for Data Augmentation
Automating data augmentation by learning a generative sequence model over user-specified transformation functions.
Research Paper
Learning to Compose Domain-Specific Transformations for Data Augmentation
Automating data augmentation by learning a generative sequence model over user-specified transformation functions.
Snorkel: Rapid Training Data Creation With Weak Supervision
This paper presents a flexible interface layer to write labeling functions based on experience.
Research Paper
Snorkel: Rapid Training Data Creation With Weak Supervision
This paper presents a flexible interface layer to write labeling functions based on experience.
Data Programming With DDLite: Putting Humans in a Different Part of the Loop
Introducing DDLite, an interactive development framework for data programming.
Research Paper
Data Programming With DDLite: Putting Humans in a Different Part of the Loop
Introducing DDLite, an interactive development framework for data programming.
