Building Data-Centric Applications with Federated Learning
Offered By: Toronto Machine Learning Series (TMLS) via YouTube
Course Description
Overview
Explore the concept of data-centric applications and federated learning in this informative talk from the Toronto Machine Learning Series. Gain insights from Roshni Malani, Engineering Leadership at Snorkel AI, and Priyal Aggarwal, Machine Learning Engineer at Snorkel AI, as they discuss how to unlock the true potential of machine learning through data collaboration. Discover how federated learning breaks down data silos by bringing model training to datasets, enabling more robust and effective machine learning models. Learn about the benefits and challenges of implementing this research concept in real-world applications through a hands-on approach. Understand the importance of data in machine learning and how traditional techniques relying on data centralization can be limiting. Delve into the practical aspects of moving federated learning from theory to product implementation in this 1 hour and 23 minute presentation.
Syllabus
Building Data Centric Applications
Taught by
Toronto Machine Learning Series (TMLS)
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