Free course13 modules

Enterprise RAG on Your Own Infrastructure

Build production RAG without cloud dependencies

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Built for CTOs, VP Engineering, senior architects, ML platform engineers

Practical AI training for CTOs, VP Engineering, senior architects, ML platform engineers

A practitioner-level course for engineering leaders who need to build, deploy, and scale Retrieval-Augmented Generation systems on infrastructure they control. From embedding models to tiered architectures, with real cost models.

This course is designed for professionals who need to move from AI curiosity to useful implementation. The lessons focus on the workflows, risks, data requirements, governance questions, and ROI arguments that teams need before putting AI into production.

Each module is written as a working guide rather than a theory note. You can read it end to end, share individual lessons with colleagues, or use the module sequence as the outline for an internal workshop.

What you'll learn

Self-hosted economics — real cost comparison of cloud RAG vs own infrastructure at TB scale

Embedding models — GTE-Qwen2, BGE-M3, Nomic Embed and when to use which

Chunking that works — semantic, structural, and multi-granularity strategies beyond 512-token splits

vLLM deployment — hardware selection, quantisation, batching, and per-query cost modelling

Tiered architecture — L1/L2/L3 caching with query routing and ambient RAG

Knowledge graphs — automatic entity and relationship extraction from documents

Outcomes

Explain where AI can help CTOs, VP Engineering, senior architects, ML platform engineers without overstating what the technology can do.

Identify the data, privacy, workflow, and governance constraints that determine whether an AI use case is ready for production.

Build a clear business case using operational metrics, implementation costs, and measurable outcomes.

Create a practical next-step plan that connects the course material to a pilot, internal training session, or stakeholder discussion.

13 modules

1Why Self-Hosted RAG 2RAG Architecture 3Embedding Models 4Vector Databases 5Document Ingestion 6Chunking Strategies 7Retrieval & Reranking 8Generation with Open Models 9vLLM Deployment 10Tiered RAG Architecture 11Knowledge Graphs 12Security & Multi-Tenancy 13Capstone Blueprint

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We'll assess your document corpus, design the pipeline, select your model stack, and build a deployment plan — tailored to your infrastructure and compliance requirements.

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