Scaling & Operating AI Systems

What you will be able to do

• You will be able to set a latency budget for an AI feature and cut p95 and tail latency using batching, streaming, and token-level tuning.
• You will be able to model the unit economics of an AI feature and right-size or route models to hit a cost-per-request target without losing quality.
• You will be able to design a layered caching strategy (exact, semantic, and prompt caching) that raises hit rate while controlling stale and wrong-hit risk.
• You will be able to scale the inference path with autoscaling, queueing, concurrency limits, and rate limiting so it stays stable under load.
• You will be able to instrument an AI system with traces, cost and quality metrics, and online evals, then trace a bad response back to its root cause.
• You will be able to engineer reliability with SLOs, timeouts, retries, circuit breakers, multi-provider failover, and graceful degradation.
• You will be able to build layered guardrails against prompt injection, abuse, and unsafe output, mapped to the OWASP LLM Top 10 and the NIST AI Risk Management Framework.
• You will be able to ship an AI feature responsibly using eval gates, staged rollout, kill switches, versioning, and governance.

What is inside

4 modules, 8 lessons. Each module ends in a graded quiz and most carry an assignment.

1. 01

   Performance and Cost Engineering

   An AI feature lives or dies on two numbers: how fast it responds and how much each call costs. This module traces where latency and spend actually go, from prompt and generation tokens to time to first token, batching, streaming, and tail latency. You will learn to set a latency budget, model unit economics, and right-size or route models to hit both targets without giving up quality.

   2 lessons · 5 quiz questions

2. 02

   Caching and Scaling the Serving Path

   Caching and elastic scaling are the two biggest levers for serving AI cheaply and fast at high volume. This module builds layered caches (exact-match, semantic similarity, and provider prompt caching), then measures hit rate against the risk of stale or wrong answers. From there you harden the inference path with autoscaling, queueing, concurrency limits, and rate limiting so it holds up when traffic spikes and stays inside upstream provider limits.

   2 lessons · 5 quiz questions · assignment

3. 03

   Observability and Reliability

   AI systems fail while the dashboards stay green, so this module builds observability and reliability for the full request path. You will instrument structured logs, distributed traces, and cost and quality metrics, add online evals and drift detection, then engineer SLOs, timeouts, retries, circuit breakers, multi-provider failover, graceful degradation, and incident response so failures are caught early and contained.

   2 lessons · 5 quiz questions

4. 04

   Guardrails and Responsible Rollout

   Responsible operation means a system that resists abuse and ships behind real gates. This module builds layered guardrails for inputs and outputs (prompt injection and jailbreak defense, moderation, PII and unsafe-content filtering) and applies least privilege to tools and agents to bound the blast radius when a defense fails. It then covers responsible rollout: eval gates that block bad releases, shadow and canary staging, kill switches, versioning, and governance mapped to the OWASP LLM Top 10, the NIST AI RMF, and the EU AI Act. You leave able to design controls that hold up under adversarial traffic and to prove, on paper, that a feature shipped behind real oversight.

   2 lessons · 5 quiz questions · assignment