Latest posts
- Applying Information Theory to ML With Fred Sala
- 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,… ... - 3 Impractical Assumptions About AI to Avoid
- 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. ... - Building Industrial-Strength NLP Applications With Ines Montani
- 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… ... - Introducing Application Studio and Announcing Our $35m Series B Funding
- Over the past year, we’ve worked hard to deliver Snorkel Flow, the first AI platform to provide all the power of machine learning without the pains of hand-labeling. Snorkel Flow lets you label data programmatically, train models flexibly, improve performance iteratively, and deploy AI applications quickly. We are incredibly proud… ... - Measuring NLP Progress With Sebastian Ruder - In this episode of Science Talks, Sebastian Ruder, Research Scientist at DeepMind, shares his thoughts on making AI practical with Snorkel AI’s Braden Hancock. This conversation covers progress made in the NLP domain with emerging research, new benchmarks like SuperGLUE, rich repositories and news sources that keep you in the… ...
- Productionizing ML Research With Thomas Wolf - 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… ...
- Debugging AI Applications Pipeline
- 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
- 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… ...
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