Data-Centric AI

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.

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.

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.

Georgetown University’s CSET is building next-generation NLP applications using Snorkel Flow to classify complex research documents. Snorkel Flow drastically reduced labeling, model training, and iteration time and better equipped CSET’s data science team to collaborate closely with analysts to gather, process, and interpret data at scale. 

The recent debut of ChatGPT astounded the public with the power and speed of foundation models, but their enterprise use remains hampered by adaptation and deployment challenges. In the past year, Snorkel AI has researched several ways to overcome those challenges. 

Snorkel AI is delighted to announce a partnership with Aimpoint Digital, a premier analytics firm specializing in AI application development that builds, operationalizes, and scales data science solutions for biopharma, manufacturing, retail, and other major industries. Aimpoint Digital leads the industry in solving complex challenges and exploiting value-generating opportunities for organizations of all sizes through data. The company helps clients

Labeling data manually can be a grind. Snorkel Flow slashes labeling time from months to minutes by allowing data scientists and domain experts collaborate through labeling functions. Snorkel Flow offers two unique capabilities that further supercharge that collaboration: Comments and Tags.

The Snorkel AI team will present five research papers advancing weak supervision and programmatic labeling at the NeurIPS 2022 conference that started this week.

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.

Introducing new capabilities for Data-centric Foundation Model Development in Snorkel Flow Powerful new large language or foundation models (FMs) like GPT-3, Stable Diffusion, BERT, and more have taken the AI space by storm, going viral—even beyond technical practitioners—thanks to incredible capabilities around text generation, image synthesis, and more. However, enterprises face fundamental barriers to using these foundation models on real,

We created Data-centric Foundation Model Development to bridge the gaps between foundation models and enterprise AI. New Snorkel Flow capabilities (Foundation Model Fine-tuning, Warm Start, and Prompt Builder) give data science and machine learning teams the tools they need to effectively put foundation models (FMs) to use for performance-critical enterprise use cases. The need is clear: despite undeniable excitement about

Databricks’ Chief Technologist: Data-Centric AI can learn from Data Engineering and ML Engineering in five ways: continuous updates, versioning, code-centric deployment, data privatization and actionable monitoring.

Create a data-centric AI application using Snorkel Flow to save your analysts time of manual labeling and information extraction related to environmental, social, and governance (ESG) factors from earnings call transcripts. Rapidly and accurately extract all existing and new factors from the transcripts to make the right investment decision.

AI is generally accepted as necessary for organizations across private and public sectors to build (or maintain) a competitive advantage. However, a major challenge to adopting AI successfully is our ability to build reliable, predictable, and equitable solutions. A critical flaw with traditional approaches to developing AI is the reliance on hand-labeled training datasets and/or “pre-trained” black-box models that are effectively ungovernable and unauditable. In this article, we explore the motivations and challenges for Trustworthy AI that we’ve encountered and discuss how core tenants of Data-Centric AI, including programmatic labeling, help ameliorate them.

To meet the requirements of unexpected regulatory changes brought on by the pandemic, a top-10 US bank needed to urgently adapt its underperforming model-centric artificial intelligence and machine learning development approach to a data-centric one. The team used Snorkel Flow to automatically classify thousands of loan documents and extract critical clauses in just 24 hours, saving loan managers thousands of hours of manual document review.

Schlumberger is the world’s leading provider of technology and services for the energy industry, operating in over 120 countries. The company provides well maintenance and analytics services to the world’s biggest oil companies, and it believes that large-scale data analysis and artificial intelligence/machine learning will help them remain a leader in the market. One way they’ve been able to achieve this is by building their own AI application using Snorkel Flow to automatically extract geological entities and critical field data across a variety of document structures and report types they receive from their customers.

This blog post introduces variants of Precision, Recall, and F1 metrics called Precision Gain, Recall Gain, and F1 Gain. The gain variants have desirable properties such as meaningful linear interpolation of PR curves and a universal baseline across tasks. This post explains what these benefits mean for you, how the gain metrics are calculated and outline some examples for intuitive comparison.