Table of Contents

• Table of Contents
• Course Overview
• Who is this course for?
• Course Breakdown: Week by Week
• Getting Started
• ✨ Beyond Traditional OCR: The SLM Advantage
• 🧠 Pipeline Architecture: Deep Dive into Throughput & Scaling
• 🔄 The Exact Document Workflow
• 🧩 Robustness: Why a Pre-layout Encoder Improves Fidelity
• 📑 Technical Report: Document Handoff
• 🏛️ Formal Architecture Assessment: Production Robustness
• 📈 Scaling Philosophy: Real-Time Workloads (Metric-Driven)
• The tech stack
• Contributors
• License

Course Overview

Most OCR tutorials stop at "call an API and get some text back." This isn't that.

Instead, we're building a production-grade, self-scaling Visual Document Understanding pipeline, deployed for real on Kubernetes (AKS or GKE), that goes far beyond flat text extraction: it reasons about charts, tables, and layout the way a human reader would — powered by a Small Language Model (Qwen 3.5) instead of a bloated frontier model.

By the end of this course, you'll have your own event-driven OCR system capable of:

• 🧠 Understanding documents, not just transcribing them — charts, tables, handwriting, and contextual reasoning via Qwen 3.5 (4B)
• ⚡ Serving generative OCR at 1.86 pages/second with vLLM's continuous batching, PagedAttention, and Multi-Token Prediction (MTP)
• 🦀 Ingesting files through a high-concurrency Rust (Axum) gateway, decoupled from GPU inference via Redis
• 🔄 Running a zero-copy, /dev/shm-based document handoff between the layout encoder and the inference engine
• ☸️ Auto-scaling T4 (layout) and A100 (inference) node pools independently with KEDA, from zero to bursting load
• 🔒 Locking the whole pipeline behind an Internal Load Balancer + Enterprise API Gateway (Azure APIM / GCP API Gateway), with zero public exposure
• 🤖 Wrapping the pipeline as an MCP server for native use by AI agents, including Claude Code

Excited? Let's get started!

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Who is this course for?

This course is for ML/AI Engineers and Platform Engineers who already know how to call an OCR API and want to know **what it takes to run one in