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…

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.

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…

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.

Continuous Model Feedback, available in beta as part of the new Studio experience, is Snorkel Flow’s latest capabilities to make training data creation and model development more integrated, automated, and guided.

Snorkel AI just hosted the second day of The Future of Data-Centric AI conference 2022. Across 40+ sessions, 50+ Data scientists, ML engineers, and AI leaders came together to share insights, best practices, and research on adopting data-centric approaches with thousands of attendees from all around the world. Aarti Bagul, a Snorkel AI ML Solutions Engineer and one of the…

Building NLP techniques to understand 10-Ks is time-consuming, costly, and challenging. In this post, Machine Learning Engineer, Aarti Bagul discusses three information extraction case studies on how banks around the world are building highly accurate NLP applications using Snorkel Flow’s AI platform. From retail banking to hedge fund investing, NLP is used across the financial industry. By processing and extracting…

Programmatic labeling moves a classic technique from interesting to high-impact So much of real-world AI development entails working with text data that’s messy — in fact, 80%+ of enterprise data is unstructured. And while state-of-the-art models get a lot of the glory, creating the training data that conveys what your model needs to learn is more often the biggest determiner of AI…

AI systems are well-suited to tasks involving recognizing and predicting data patterns. Supervised classification systems categorize unseen data into a finite set of discrete classes by learning from millions of hand-labeled labeled sample points. These classifiers are powerful business tools – they automate document sorting, customer sentiment analysis, sales performance, and other distinct business problems. However, they also require an…