Model Openness Framework: The Path to Openness, Transparency and Collaboration in Machine Learning Models
Offered By: CNCF [Cloud Native Computing Foundation] via YouTube
Course Description
Overview
Explore the Model Openness Framework (MOF) in this insightful conference talk by Matt White from The Linux Foundation and Anni Lai from Futurewei. Delve into the challenges of transparency, reproducibility, and safety in Generative AI (GAI) commercialization. Learn about the MOF's ranked classification system for evaluating machine learning models based on completeness and openness. Discover how this framework addresses concerns about misrepresentation of open models and guides researchers in providing model components under permissive licenses. Gain insights into the Model Openness Tool demonstration and understand the benefits of MOF for both model producers and consumers. Examine how widespread adoption of MOF can foster a more open AI ecosystem, benefiting research, innovation, and the adoption of state-of-the-art models.
Syllabus
Model Openness Framework: The Path to Openness, Transparency and Collabor... - Matt White & Anni Lai
Taught by
CNCF [Cloud Native Computing Foundation]
Related Courses
Data Science in Action - Building a Predictive Churn ModelSAP Learning Applied Data Science Capstone
IBM via Coursera Data Modeling and Regression Analysis in Business
University of Illinois at Urbana-Champaign via Coursera Introduction to Predictive Analytics using Python
University of Edinburgh via edX Machine Learning con Python. Nivel intermedio
Coursera Project Network via Coursera