MLOps

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

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

Diving Into SliceLine – 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, Kaushik Shivakumar dives into

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:

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.

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 Snorkel AI team. You

In this episode of ScienceTalks, Snorkel AI’s Braden Hancock Hugging Face’s Chief Science Officer, Thomas Wolf. Thomas shares his story about how he got into machine learning and discusses important design decisions behind the widely adopted Transformers library, as well as the challenges of bringing research projects into production. ScienceTalks is an interview series from Snorkel AI, highlighting some of the best work and ideas to make AI practical.

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

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.

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

We love meeting people in the data science and machine learning community. Here are a few upcoming events where you can meet Snorkelers.

There’s a lot of excitement about the potential for AI to improve healthcare. This is driven by compelling advances across a wide range of applications including drug discovery, radiology, pathology, electronic medical record (EMR) intelligence, clinical trials, and more. There are also many challenges for development and deployment of AI for healthcare.

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