Team Snorkel

The founding team of Snorkel AI has spent over half a decade—first at the Stanford AI Lab and now at Snorkel AI—researching data-centric techniques to overcome the biggest bottleneck in AI: The lack of labeled training data. In this video Snorkel AI co-founder Paroma Varma gives an overview of the key principles of data-centric AI development. What is data-centric AI?…

Showcasing Liger—a combination of foundation model embeddings to improve weak supervision techniques. Machine learning whiteboard (MLW) open-source series In this talk, Mayee Chen, a PhD student in Computer Science at Stanford University focuses on her work combining weak supervision and foundation model embeddings that improve two essential aspects of current weak supervision techniques. Check out the full episode here or…

How can data-centric AI speeds your end-to-end healthcare AI development and deployment Healthcare is a field that is awash in data, and managing it all is complicated and expensive. As an industry, it benefits tremendously from the ongoing development of machine learning and data-centric AI. The potential benefits of AI integration in healthcare can be broken down into two categories:…

The Future of Data-Centric AI Talk Series Background Chelsea Finn is an assistant professor of computer science and electrical engineering at Stanford University, whose research has been widely recognized, including in the New York Times and MIT Technology Review. In this talk, Chelsea talks about algorithms that use data from tasks you are interested in and data from other tasks….

The future of data-centric AI talk series Background Anima Anandkumar holds dual positions in academia and industry. She is a Bren professor at Caltech and the director of machine learning research at NVIDIA. Anima also has a long list of accomplishments ranging from the Alfred P. Sloan scholarship to the prestigious NSF career award and many more. She recently joined…

Understanding the label model. Machine learning whiteboard (MLW) open-source series Background Frederic Sala, is an assistant professor at the University of Wisconsin-Madison, and a research scientist at Snorkel AI. Previously, he was a postdoc in Chris Re’s lab at Stanford. His research focuses on data-driven systems and weak supervision. In this talk, Fred focuses on weak supervision modeling. This machine…

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…

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…