Federated Learning and Privacy-preserving RAGs
Learn federated learning and privacy-preserving techniques. This course will teach you how to architect AI solutions while ensuring data privacy in Retrieval-Augmented Generation (RAG) systems.
What you'll learn
More and more organizations would like to implement Retrieval-Augmented Generation (RAG) solutions to enhance their customer experience integrating privacy-preserving techniques ensuring data security and regulatory compliance.
In this course, Federated Learning and Privacy-preserving RAGs, you’ll learn to design and implement advanced AI systems that prioritize data privacy without sacrificing performance. First, you’ll explore the fundamentals of federated learning, including its principles and how it enables decentralized data processing. Next, you’ll discover how to integrate privacy-preserving techniques into RAG models, such as homomorphic encryption and differential privacy, to safeguard sensitive information. Finally, you’ll learn to implement these concepts practically, developing and deploying RAG systems that adhere to privacy regulations and protect user data. When you’re finished with this course, you’ll have the skills and knowledge needed to create robust, privacy-conscious RAG solutions that enhance AI performance while maintaining strict data protection standards.
Table of contents
About the author
Luca Berton, with over 18 years in AI, cloud, and automation, is a leading expert in Ansible automation, renowned for his practical experience and educational impact. His career includes a significant role as Vice President of Infrastructure at JPMorgan Chase & Co., where he spearheaded automation initiatives to shape future corporate infrastructure. Luca’s journey also features pioneering Smart City solutions at City Green Light and architecting scalable Amazon EC2 Linux instances at Studio Sto... more