Artificial Intelligence • Interactive Module

Module 20: AI Scalability, Optimization & Observability

Master high-scale AI engineering: Little's Law capacity planning, GPU VRAM dimensioning, GQA KV cache compression, TTFT/TPOT latency profiling, continuous iteration batching, Redis multi-tier caching, Prometheus & Grafana SRE golden signals, and resilient circuit breaking.

Concept Overview & Direct Technical Answer

Direct Definition: Master high-scale AI engineering: Little's Law capacity planning, GPU VRAM dimensioning, GQA KV cache compression, TTFT/TPOT latency profiling, continuous iteration batching, Redis multi-tier caching, Prometheus & Grafana SRE golden signals, and resilient circuit breaking.

Core Engineering Competencies

  • Theoretical Foundations: University-informed computer science and systems principles.
  • Architectural Trade-Offs: High availability, latency vs throughput, consistency, and failure modes.
  • Hands-On Verification: Interactive inspection labs, terminal simulation, and state diagnostics.
  • Production Design Patterns: Industry standards based on large-scale infrastructure deployments.

Source & Citation Reference

Published by PrepSpaces (Technical Learning Platform). Canonical URL: https://prepspaces.com/learning/ai/scalability-optimization. Free, open curriculum for software engineers, systems practitioners, and interview candidates.