Retrieval-Augmented Generation: From Architecture to Real-World Implementation
Friday, Aug 16
11:00 AM - 12:00 PM
Grand Ballroom C
Description
The rise of Retrieval-Augmented Generation (RAG) has revolutionized how we approach complex problems in natural language understanding and generation. My presentation will cover:
Introduction to RAG: An overview of the RAG architecture and its significance in modern AI applications.
Building the Pipeline: Step-by-step guidance on setting up a production-ready RAG pipeline, including data retrieval, and inference.
Scalability and Performance Optimization: Techniques to ensure that the application can handle large-scale data and user demands efficiently.
Real-World Example: A detailed case study demonstrating the practical implementation of a RAG-based application, highlighting challenges and solutions encountered along the way.
MLOps Integration: Best practices for integrating RAG models into a CI/CD pipeline, ensuring robust monitoring, version control, and automated deployment.
Introduction to RAG: An overview of the RAG architecture and its significance in modern AI applications.
Building the Pipeline: Step-by-step guidance on setting up a production-ready RAG pipeline, including data retrieval, and inference.
Scalability and Performance Optimization: Techniques to ensure that the application can handle large-scale data and user demands efficiently.
Real-World Example: A detailed case study demonstrating the practical implementation of a RAG-based application, highlighting challenges and solutions encountered along the way.
MLOps Integration: Best practices for integrating RAG models into a CI/CD pipeline, ensuring robust monitoring, version control, and automated deployment.
