Description
Advanced RAG Engineering: Build Production-Ready Enterprise is a course on designing, developing, and deploying enterprise-level Retrieval Augmented Manufacturing (RAG) systems published by Udemy Online Academy. Designed for AI engineers, software developers, data engineers, and technical architects, this program goes beyond basic RAG concepts to address the challenges of building reliable AI applications for large-scale business environments. In this hands-on course, you will build an enterprise knowledge intelligence platform that evolves throughout the course. Learners will explore enterprise data ingestion, advanced document processing, intelligent segmentation, embeddings, vector and combinatorial search, re-ranking, query optimization, security, access control, evaluation, observability, scalability, and production deployment.
You will start with a basic RAG application and gradually add advanced capabilities of ingestion, chunking, retrieval, query optimization, adaptive workflows, graph-based retrieval, multimodal document processing, assessment, observability, security, and deployment. This course is designed for AI engineers, machine learning engineers, software developers, data engineers, platform engineers, solution architects, MLOps specialists, and technical leaders who already understand the basic concepts of RAG and want to build reliable enterprise AI systems.
What you will learn in Advanced RAG Engineering: Build Production-Ready Enterprise:
- Design enterprise RAG architectures that balance retrieval quality, grounding, latency, cost, security, governance, and maintainability.
- Build local ingestion pipelines for PDFs, HTML, Office files, spreadsheets, tables, and scans with metadata, versioning, deduplication, and lineage.
- Apply fixed, recursive, structure-aware, semantic, hierarchical, and parent-child chunking to improve retrieval and context quality.
- Implement hybrid retrieval with keyword search, dense embeddings, metadata filters, Reciprocal Rank Fusion, and CPU-based re-ranking.
- Improve retrieval with query rewriting, multi-query generation, decomposition, HyDE, intent checks, and retrieval routing.
- Build Self-RAG and Corrective RAG workflows with grading, correction, verification, retry limits, cost controls, and abstention.
- Create CPU-friendly GraphRAG pipelines with entities, relationships, resolution, provenance, graph traversal, and multi-hop retrieval.
- Build CPU-only retrieval for OCR, layout-aware parsing, tables, figures, page-level search, modality routing, and visual citations.
- Evaluate RAG systems with golden datasets, retrieval metrics, grounding checks, citation tests, local judges, and regression suites.
- Prepare RAG systems for production with tracing, caching, cost estimation, ACL-aware retrieval, tenant isolation, security, CI/CD, and deployment.
- and …
Course specifications
Publisher: Udemy
Instructors: Arjun Vaid and School of AI
Language: English
Level: Introductory to Advanced
Number of Lessons: 83
Duration: 20h 36m
Course topics
Advanced RAG Engineering: Build Production-Ready Enterprise Prerequisites
Intermediate Python programming experience is recommended.
Basic familiarity with large language models, embeddings, vector search, and standard RAG workflows will be helpful.
Familiarity with REST APIs, JSON, Git, command-line tools, and basic Docker usage is recommended.
A computer capable of running Python and local Docker services is required; 16 GB of system memory is recommended.
No paid AI API, cloud account, managed database, or dedicated GPU is required for the mandatory course demonstrations and Hands on Labs.
Pictures
Advanced RAG Engineering: Build Production-Ready Enterprise introduction video
Installation guide
After Extract, watch with your favorite Player.
Subtitle: None
Quality: 1440p
Downloadly link
Rapidgator link
File password (s): www.downloadly.ir
Size
21.8 GB