Combining foundation model outputs with weak supervision yields faster model development and requires fewer ground truth labels.

Snorkel AI CEO and Co-Founder Alex Ratner’s introduction to data-centric AI from the 2022 Future of Data-Centric AI virtual conference.

Credo AI’s head of data science explains at Snorkel’s FDCAI 2022 how his team works to operationalize responsible AI assessment tools.

Brown professor Stephen Bach tells Snorkel CEO Alex Ratner about his research into improving foundation models like GPT-3 with curated data.

Cleanlab Co-Founder and CEO Curtis Northcutt presents his company’s automatic, universal and open-source tools to quickly clean data sets.

Understanding and quantifying people’s opinions has become increasingly important to businesses, but the way people can express multiple thoughts in the same sentence has frustrated practitioners’ efforts to extract those opinions cleanly—a problem we can solve through aspect-based sentiment analysis (ABSA).

Jan Neumann, Vice President of Machine Learning for Comcast Applied AI and Discovery, describes Comcast’s data-centric AI approach to speech.

Meta senior applied research manager Anoop Sinha and Snorkel AI co-founder Braden Hancock discuss mastering speech and search with TWIML host Sam Charrington.

As part of Snorkel AI’s partnership with Snowflake, users can now upload millions of rows of data seamlessly from their Snowflake warehouse into Snorkel Flow via the natively-integrated Snowflake connector. With a few clicks, a user can upload massive amounts of Snowflake data and quickly develop high-quality ML models using Snorkel Flow’s Data-Centric AI platform.

Anirudh Koul is Machine Learning Lead for the NASA Frontier Development Lab and the Head of Machine Learning Sciences at Pinterest. He presented at Snorkel AI’s 2022 Future of Data Centric AI (FDCAI) Conference.

Snorkel AI is teaming up with Google Cloud to help F500 companies and AI innovators solve their most difficult problems.

As machine learning practitioners, few of us would expect the first version of a new model to achieve our objective. We plan for multiple rounds of iteration to address errors and improve performance, and the Snorkel Flow platform provides tools to enable this kind of iteration within the data-centric AI framework.

Together, Snorkel AI and Seldon enable enterprises to adopt AI across the business at scale by dramatically accelerating development and deployment and tightening the feedback loop to rapidly respond to data drift or changing business requirements.

Most poll respondents at Snorkel AI’s recent Foundation Model Virtual Summit named questionable accuracy as the biggest barrier preventing them from getting organizational value from Foundation Models.

Snorkel CEO Alex Ratner interviews Mayee Chen about how Liger improves the effectiveness of programmatic labeling through foundation model embeddings.

Snorkel AI has teamed with Snowflake to help our shared customers transform raw, unstructured data into actionable, AI-powered insights.

Hamsa Bastani presented a summary of her and her co-authors’ ongoing work using machine learning and Snorkel AI’s tools to detect and track activities that are associated with a high risk for global sex trafficking.

Snorkel AI co-founder and CEO Alex Ratner recently interviewed several Snorkel researchers about their published academic papers. In this video, Alex talks with Ryan Smith, Senior Applied Scientist at Snorkel, about the work he did on using foundation models to build compact, deployable, and effective models.

Snorkel AI held its Foundation Model Summit Jan 17, bringing together 12 presenters and over 600 attendees at 10 virtual sessions. The event drew registrants from across many sectors, including the tech industry, healthcare, and financial services.

Snorkel AI co-founder and CEO Alex Ratner talks with Ananya Kumar about the work he did on improving the effectiveness of foundation models by using contrastive learning, image augmentations, and labeled subsamples.

While a majority of Natural Language Processing (NLP) models focus on English, the real world requires solutions that work with languages across the globe. This demo shows how effectively users can build cross-language models in Snorkel Flow.

Using Snorkel Flow, Pixability has created a way to build classifiers for massive amounts of YouTube data quickly—that was previously out of reach.

Sirisha Rella, Technical Product Marketing Manager at Nvidia, recently gave a Lightning Talk presentation on “demystifying” speech AI at Snorkel AI’s Future of Data-Centric AI virtual conference.

Snorkel AI will hold a free Foundation Model Virtual Summit on Tuesday, January 17 where speakers from across the technology industry, including some from Google and Stanford University, will discuss the enterprise use of Foundation Models.

Snorkel Flow debuts a new integration with Microsoft Azure Form Recognizer to help organizations leverage Azure AI services.

Researcher Simran Arora tells Snorkel CEO Alex Ratner how she improved foundation model effectiveness by using “Ask Me Anything”-style questions.

See what’s in our latest Snorkel Flow release and how we’re accelerating data-centric AI development further.

More components in an ML lifecycle are designed to run on autopilot, but some tasks require human-in-the-loop ML, an active research topic that has seen an increasing number of publications in the last 10 years.

A central innovation team at a top US bank wanted to modernize its AI development and data annotation processes in order to create a custom natural language processing (NLP) model that could extract important financial information from 10-Ks. Manually reviewing these documents was taking up valuable time that could be better spent assisting customers. The team used Snorkel Flow’s data-centric AI development process and programmatic labeling to train a customized NLP model that could accurately extract information on interest rate swaps.

MIT’s Technology Review reported this week that workers in Venezuela contracted by outsourced data annotation services provider shared customer data—low-angled pictures intended to be labeled, including one that featured a woman in a private moment in the bathroom—with each other on social media. Programmatic labeling could have minimized this.