Deploying and Maintaining RAG Systems
Retrieval Augmented Generation (RAG) is a powerful generative AI application pattern that generates relevant and contextually appropriate responses. This course will teach you to effectively build, deploy, and manage an end-to-end RAG system.
What you'll learn
Despite the tremendous potential of pre-trained LLMs, they fall short of generating contextual response data, especially on private data. Retrieval Augmented Generation (RAG) is a popular approach to tackle this limitation.
In this course, Deploying and Maintaining RAG System, you’ll learn to build, deploy, and manage an end-to-end RAG system.
First, you’ll explore the critical components of the RAG system and see it in action using popular open-source frameworks.
Next, you’ll discover standard deployment blueprints for RAG systems and key considerations for managing RAG systems.
Finally, you’ll learn about the limitations of simple RAG systems and ways to handle them.
When you’re finished with this course, you’ll have the skills and knowledge of RAG systems needed to leverage its full potential.
Table of contents
About the author
Abhishek Kumar is a data science consultant, author, and Google Developers Expert (GDE) in machine learning. He holds a master’s degree from the University of California, Berkeley, and has been featured in the "Top 40 under 40 Data Scientist" list. He is also a public speaker and has delivered talks in top data conferences across the globe including Strata Data, AI conference, ODSC, and Fifth Elephant. His focus area is machine learning and deep learning at scale and is also a recipient of the... more