Data-centric Foundation Model Development: Bridging the gap between foundation models and enterprise AI

Introducing new capabilities for Data-centric Foundation Model Development in Snorkel Flow Powerful new large language or foundation models (FMs) like GPT-3, Stable Diffusion, BERT, and more have taken the AI space by storm, going viral—even beyond technical practitioners—thanks to incredible capabilities around text generation, image synthesis, and more. However, enterprises face fundamental barriers to using these foundation models on real,…

Alex Ratner
November 17, 2022

Latest posts

  • Debugging AI Applications Pipeline
    February 3, 2021
    - We’ll analyze major sources of errors during the four steps of building AI applications: data labeling, feature engineering, model training, and model evaluation. ...
  • How To Overcome Practical Challenges for AI in the Public Sector
    January 7, 2021Charlie Greenbacker
    - 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… ...
  • How To Overcome Practical Challenges for AI in Finance
    December 29, 2020Manas Joglekar
    - 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… ...
  • 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… ...
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