APX AI
Online
This course provides comprehensive instruction on architecting, implementing, and optimizing large-scale distributed Retrieval-Augmented Generation (RAG) systems. It covers advanced techniques for distributed retrieval, LLM integration at scale, efficient data pipelines, and operational best practices for production environments. Participants will acquire the skills to build high-performance, resilient, and cost-effective RAG solutions capable of handling massive datasets and complex information retrieval tasks.
Prerequisites Advanced RAG, Distributed Systems
Level:
Distributed RAG System Design
Architect highly scalable and resilient RAG systems using distributed computing principles.
Advanced Retrieval at Scale
Implement and optimize state-of-the-art retrieval techniques for massive datasets, including sharded vector search and hybrid models.
LLM Optimization for RAG
Apply advanced methods for fine-tuning, serving, and managing LLMs within large-scale RAG pipelines.
Scalable Data Pipelines
Construct and manage robust data ingestion, processing, and embedding generation pipelines for distributed RAG.
Operationalizing RAG Systems
Deploy, monitor, and maintain large-scale RAG systems using MLOps best practices and cloud-native technologies.
Advanced RAG Architectures
Develop and implement sophisticated RAG patterns such as multi-hop, iterative, and agentic RAG for complex information needs.
Performance Engineering for RAG
Analyze, benchmark, and tune distributed RAG systems for optimal latency, throughput, and cost-efficiency.