Data Development

Snorkel Flow’s Spring 2023 release focuses on adapting foundation models for enterprise use—including fine-tuning and additional features.

Physician notes and other sources of unstructured data can offer insight into drugs’ negative side effects. Here’s how modern machine learning tools can help turn all that data into a useful resource.

McKinsey’s Carlo Giovine and David Harvey present “Trends in Enterprise ML and the potential impact of Foundation Models” at Snorkel’s Foundation Model Summit.

Dillon Laird, engineering manager at Landing AI, presents on LandingLens and democratizing AI at Snorkel AI’s 2022 FDCAI Conference.

Snorkel AI, Google Cloud and Vertex AI partner to help organizations transform data into AI-powered systems faster than ever.

Amanpreet Singh, Lead Researcher at Hugging Face gave a presentation entitled Towards Unified Foundation Models for Vision and Language Alignment a Snorkel AI’s Foundation Model Summit in January.

Foundation Models (FMs), such as GPT-3 and Stable Diffusion, mark the beginning of a new era in machine learning and artificial intelligence. What are they and how will they impact your business? Find out in our guide.

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.

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).

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.

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

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.

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 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.

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.

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

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. 

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

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.

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.