Phase 8: Production Hardening & Operations · 50 min · Python · Redis · Kubernetes

Reliability Engineering for AI

Retries, fallbacks, circuit breakers. The AI system stays up.

What you will learn

Implement retry patterns: exponential backoff, jitter, max retries, retryable vs. non-retryable errors
Configure fallbacks: model fallback (GPT-4o → Claude → Llama), retrieval fallback (vector → keyword → cached)
Build circuit breakers: detecting cascading failures, opening/closing circuits, half-open recovery
Implement graceful degradation: partial responses, reduced functionality modes, maintenance mode
Configure rate limiting: per-user, per-tenant, global, token bucket vs. sliding window, 429 handling

Objective

What you'll learn

This lesson takes approximately 50 min. By the end, you will be able to:

Implement retry patterns: exponential backoff, jitter, max retries, retryable vs. non-retryable errors
Configure fallbacks: model fallback (GPT-4o → Claude → Llama), retrieval fallback (vector → keyword → cached)
Build circuit breakers: detecting cascading failures, opening/closing circuits, half-open recovery
Implement graceful degradation: partial responses, reduced functionality modes, maintenance mode
Configure rate limiting: per-user, per-tenant, global, token bucket vs. sliding window, 429 handling

The Problem

AI systems fail in ways traditional software doesn't: model providers rate-limit you, inference times spike unpredictably, and LLM non-determinism means the same input can produce different outputs. Reliability engineering for AI means building systems that stay up: retries with exponential backoff, model fallbacks, circuit breakers, and rate limiting.

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You've read the first 2 sections. The rest of this lesson covers The Concept, Build, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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Key terms in this lesson

Token