Latest posts

• Machine Learning Production Myths
  December 23, 2020Chip Huyen
  - 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… ...
• Meet a Snorkeler at an Upcoming Event
  November 17, 2020Team Snorkel
  - We love meeting people in the data science and machine learning community. Here are a few upcoming events where you can meet Snorkelers. ...
• How to Overcome Practical Challenges for AI in Healthcare
  November 9, 2020Brandon Yang
  - 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… ...
• Every Vote Counts
  October 12, 2020Team Snorkel
  - Our mission at Snorkel AI is to make artificial intelligence practical with an end-to-end machine learning platform that focuses centrally on training data. Our technology has been used for incredibly important efforts – from solving medical challenges with Stanford Medicine to identifying prejudiced language in social media. We’re just getting… ...
• Snorkel AI Welcomes Devang Sachdev as Vice President of Marketing
  July 28, 2020Alex Ratner
  - 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… ...
• Snorkel AI: Putting Data First in ML Development
  July 14, 2020Alex Ratner
  - 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… ...