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…

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…

Enabling iterative development workflows with Snorkel Flow’s Application Studio. Consider this scenario— we’re AI engineers, and we’re building a social media monitoring application to track the sentiment of Fortune 500 company mentions in the news.

ScienceTalks with Saam Motamedi We at Snorkel AI have received many requests from data scientists and machine learning engineers who aspire to be founders, where do they start and how should they get started on their entrepreneurial journey? We genuinely believe that data scientists and machine learning engineers will build the next generation of mega-enterprises. Over the summer, we’ve recorded…

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…

In this episode of Science Talks, Explosion AI’s Ines Montani sat down with Snorkel AI’s Braden Hancock to discuss her path into machine learning, key design decisions behind the popular spaCy library for industrial-strength NLP, the importance of bringing together different stakeholders in the ML development process, and more.This episode is part of the #ScienceTalks video series hosted by the…

We’ll analyze major sources of errors during the four steps of building AI applications: data labeling, feature engineering, model training, and model evaluation.

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.

Takeaways from MLSys Seminars with Chip HuyenIn November, I had the opportunity to come back to Stanford to participate in MLSys Seminars, a series about Machine Learning Systems. It was great to see the growing interest of the academic community in building practical AI applications. Here is a recording of the talk.The talk was originally about the principles of good…