Data-Centric AI

The future of data-centric AI talk series Background An AI system consists of two parts: the model— algorithm or some code—and data. The dominant paradigm in machine-learning researchers has been for most data scientists, including myself, to download a fixed dataset and iterate on the model. That this has become conventional is a tribute to how successful this model-centric approach

Using a data-centric approach to capture the best of rule-based systems and ML models for enterprise AI One of the biggest challenges to making AI practical for the enterprise is keeping the AI application relevant (and therefore valuable) in the face of ever-changing input data and evolving business objectives. Practitioners typically use one of two approaches to build these AI applications:

Genentech, a global biotech leader and member of the Roche Group, leveraged Snorkel Flow to extract critical information from lengthy clinical trial protocol (CTP) pdf documents. They built AI applications that used NER, entity linking, text extraction, and classification models to determine inclusion/ exclusion criteria and to analyze Schedules of Assessments. Genentech’s team achieved 95-99% model accuracy by using Snorkel

The future of data-centric AI talk series Background Michael DAndrea is the Principal Data Scientist at Genentech. He earned his MBA from Cornell University and a Master’s degree in Computing and Education from Columbia University. He currently works on using unstructured data sources for clinical trial analytics and his team is partnered with the Stanford “AI For Health” initiative as

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

The Future of Data-Centric AI Talk Series Background Alex Ratner is CEO and co-founder of Snorkel AI and an Assistant Professor of Computer Science at the University of Washington. He recently joined the Future of Data-Centric AI event, where he presented the principles of data-centric AI and where it’s headed. If you would like to watch his presentation in full,

The Snorkel AI founding team started the Snorkel Research Project at Stanford AI Lab in 2015, where we set out to explore a higher-level interface to machine learning through training data. This project was sponsored by Google, Intel, DARPA, and several other leading organizations and the research was represented in over 40 academic conferences such as ACL, NeurIPS, Nature and

The Future of Data-Centric AI Talk Series Background Roshni Malani received her PhD in Software Engineering from the University of California, San Diego, and has previously worked on Siri at Apple and as a founding engineer for Google Photos. She gave a presentation at the Future of Data-Centric AI virtual conference in September 2021. Her presentation is below, lightly edited

Machine Learning Whiteboard (MLW) Open-source Series We launched the machine learning whiteboard series (MLW) was launched earlier this year as an open-invitation forum to brainstorm ideas and discuss the latest papers, techniques, and workflows in artificial intelligence. Everyone interested in learning about machine learning can participate in an informal and open environment. If you are interested in learning about ML,

The Future of Data-Centric AI Talk Series Background Snorkel co-founder Chris Ré is an associate professor of Computer Science at Stanford University and an award-winning researcher in data-based theory and machine learning. He has co-founded four companies based on his research in machine learning systems. Chris recently presented at the Future of Data-Centric AI virtual event in September, where he

Main takeaways from The Future of Data-Centric AI Event We recently hosted The Future of Data-Centric AI, where academia, research, and industry experts and practitioners came together to discuss the shift from model-centric AI development to data-centric AI and what lies ahead. This post gives you a quick overview of the event and top takeaways from over eight hours of

Defining and Building Malleable ML Systems – Machine Learning Whiteboard (MLW) Open-Source Series As you may know, earlier this year, we started our machine learning whiteboard (MLW) series, an open-invite space to brainstorm ideas and discuss the latest papers, techniques, and workflows in the AI space. We emphasize an informal and open environment to everyone interested in learning about machine learning. In this

Frontend Development Best Practices for Working With Lots of Data From Snorkel AI Engineering As a frontend engineer, it’s often easy to run into limitations when scaling large applications. At Snorkel AI, we often run into times where our users work with data that scales into the gigabytes when using Snorkel Flow. We have built Snorkel Flow around two core

Snorkel Flow LTS Release Summer ‘21 By adopting Snorkel Flow, a data-centric AI development platform powered by programmatic labeling, our customers have changed how they build and deploy AI applications. We’ve seen our customers save tens-of-millions of dollars in manual labeling costs and person-years of time by applying weak supervision with Snorkel Flow.Over the last few months, we’ve been hard

ScienceTalks with Paroma Varma In this episode of Science Talks, Snorkel AI’s Braden Hancock chats with Paroma Varma – a co-founder of Snorkel AI and one of the first and leading contributors to the Snorkel project. We discuss Paroma’s path into machine learning, her work in optimization and signal processing during her undergrad, weak supervision and image data during her

Join the live discussion. Learn how to unlock data-centric AI and make AI development practical in your organization Working with vast unstructured and unlabeled data is one of the bottlenecks in the machine learning lifecycle. Machine learning models can only get as reliable and accurate as the data being fed to them. With a data-centric approach 1, your data science

We started the Snorkel project at the Stanford AI lab in 2015 around two core hypotheses:

Machine Learning Whiteboard (MLW) Open-source Series Earlier this year, we started our machine learning whiteboard (MLW) series, an open-invite space to brainstorm ideas and discuss the latest papers, techniques, and workflows in the AI space. We emphasize an informal and open environment to everyone interested in learning about machine learning.In this episode, Manan Shah dives into “Glean: Structured Extractions from

The how, what, and why of Snorkel’s programmatic data labeling approach and the state-of-the-art Snorkel Flow platform. The year was 2015. For the first time, machine learning (ML) had outperformed humans in the annual ImageNet challenge.

In this episode of Science Talks, Snorkel AI’s Braden Hancock chats with Jason Fries – a research scientist at Stanford University’s Biomedical Informatics Research lab and Snorkel Research, and one of the first contributors to the Snorkel open-source library. We discuss Jason’s path into machine learning, empowering doctors and scientists with weak supervision, and utilizing organizational resources in biomedical applications of Snorkel. This episode is part

In this episode of Science Talks, Frederic Sala – an assistant professor of Computer Science at the University of Wisconsin Madison and a research scientist at Snorkel discusses his path into machine learning, the central thesis that ties together his multidisciplinary research, his thoughts on the future of weak supervision, as well as his decision to go into academia.

Impractical ML assumptions are made every day in research, which limit its adoption. In the real world, these assumptions do not hold up. Learn more about how to avoid making these assumptions about AI application development.

Over the past year, we’ve worked hard to deliver Snorkel Flow, the first AI platform to provide all the power of machine learning without the pains of hand-labeling. Snorkel Flow lets you label data programmatically, train models flexibly, improve performance iteratively, and deploy AI applications quickly. We are incredibly proud of the value that our customers, including two of the

AI is already transforming the business of government. But the positive impacts of this transformation, from increasing the efficiency of public services to enhancing the effectiveness of tax dollars, are still in the earliest stages. Public sector organizations generally have access to the same talent, software models, and hardware infrastructure as any private sector company, but they face a number of relatively unique practical challenges that hinder their operationalization of AI.

Advancements in artificial intelligence promise efficiency gains for financial institutions. AI-powered applications can revolutionize an organization’s risk management, fraud detection, compliance monitoring, and other processes. Financial services companies have smart data scientists and good infrastructure needed for deploying AI. But their ability to rapidly develop and deploy AI applications is hampered by several unique challenges.

We are inventing a new way to build enterprise AI applications. Taking a data-centric approach, we are making machine learning iterable, faster to deploy, and ultimately more practical.That is a fantastic opportunity, but it also presents one of our biggest challenges – figuring out how to bridge the gap between developers at the vanguard of machine learning and business leaders

Today I’m excited to announce Snorkel AI’s launch out of stealth! Snorkel AI, which spun out of the Stanford AI Lab in 2019, was founded on two simple premises: first, that the labeled training data machine learning models learn from is increasingly what determines the success or failure of AI applications. And second, that we can do much better than labeling this