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Architectural Deep-Dive into Production AI Systems

For engineers who want to understand how production AI systems are actually built

Half-day workshopWorkshop
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Program Overview

What This Program Covers

Most AI education focuses on using AI tools — this program focuses on the architecture of the systems underneath them. Learn how production AI systems are designed, how inference infrastructure works, how to architect AI features for reliability and scale, and how the leading AI companies build the systems that power modern AI products.

What You'll Learn

  1. 1Understand the architecture of large language model inference systems
  2. 2Design AI application architectures for reliability and scale
  3. 3Apply caching, batching, and optimization patterns for AI workloads
  4. 4Architect multi-modal AI systems that handle text, images, and more
  5. 5Design evaluation and monitoring infrastructure for AI systems
  6. 6Understand GPU architecture and its implications for AI system design
  7. 7Apply security and privacy patterns to AI system architecture

Outline

Program Snapshot

Module 1 — LLM Inference Architecture

  • ›How LLM inference actually works
  • ›GPU architecture and memory constraints
  • ›Batching, caching, and throughput optimization
  • ›Hands-on: analyze a production AI architecture

Module 2 — AI Application Architecture

  • ›Designing for reliability with AI components
  • ›Fallback patterns and graceful degradation
  • ›Cost architecture for AI workloads
  • ›Hands-on: design a production AI application

Module 3 — Evaluation and Observability

  • ›Evaluation frameworks for AI systems
  • ›Monitoring AI quality in production
  • ›A/B testing AI components
  • ›Hands-on: build an AI evaluation pipeline

Module 4 — Advanced Topics

  • ›Multi-modal system architecture
  • ›Security and privacy in AI systems
  • ›The frontier — what's coming next
  • ›Career paths in AI systems engineering

Who This Is For

  • Senior and staff software engineers
  • Platform engineers building AI infrastructure
  • Engineering leads evaluating AI architecture
  • Architects designing AI-powered products

Prerequisites

  • Strong software engineering fundamentals
  • Experience with distributed systems
  • Basic familiarity with LLM APIs preferred

Bring This Program to Your Team

Every bILTup program is fully customized to your team's tech stack, goals, and timeline. Tell us about your team and we'll design something built specifically for you.

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