- Course
- AI
Scaling Methods for RAG Systems
Scaling a RAG system requires efficient distributed computing and load balancing. This course will teach you how to scale your RAG solution for production readiness using PyTorch, AWS ECS, and caching for optimized performance.
What you'll learn
Scaling a Retrieval-Augmented Generation (RAG) system for production requires overcoming challenges in distributed computing, parallel processing, and load balancing. In this course, Scaling Methods for RAG Systems, you’ll learn to scale your RAG solution for production readiness. First, you’ll explore the principles of parallel processing and distributed computing with PyTorch. Next, you’ll discover how to implement load balancing using AWS ECS. Finally, you’ll learn how to optimize performance through caching and memory management. When you’re finished with this course, you’ll have the skills and knowledge of RAG scaling needed to deploy robust, production-ready systems.
Table of contents
About the author
Axel Sirota has a Masters degree in Mathematics with a deep interest in Deep Learning and Machine Learning Operations. After researching in Probability, Statistics and Machine Learning optimization, he is currently working at JAMPP as a Machine Learning Research Engineer leveraging customer data for making accurate predictions at Real Time Bidding.