Phase 7: Multi-Model Pipeline Orchestration · 50 min · Python · Temporal

Error Handling & Step-Level Resume

Retry the failed step, not the entire pipeline. Resume from the last successful checkpoint. That's production-grade error handling.

What you will learn

Implement step-level retry: retry individual failed steps, not the entire pipeline
Implement partial failure recovery: resume from last successful step
Design idempotent pipelines: same input always produces same output (deterministic seeds)
Classify errors: transient (retry), content policy (route to different provider), quality (regenerate), fatal (alert human)

Objective

Compose media stages with explicit state, routing, retry, cost, and quality boundaries. In the paired studio lab, build a 5-step pipeline (generate image → remove background → upscale → quality check → deliver) with step-level error handling. Simulate a failure at step 3, demonstrate resume from step 3 without re-running steps 1-2. Include error classification (transient, content policy, quality, fatal) and appropriate handling for each.

The Problem

A 9-step pipeline fails at step 7 (post-production). Without step-level resume:

Re-run the entire pipeline from step 1
Re-pay for steps 1-6 ($0.70 in API costs)
Wait 120s for steps 1-6 to re-execute
If step 7 fails again, repeat

With step-level resume:

Load checkpoint — steps 1-6 are already completed
Retry only step 7
Cost: $0 (steps 1-6 already done)
Time: 10s (only step 7)

Step-level error handling and resume is an advanced production skill — it shows you've operated generative media systems at scale.

What you'll build
Implement step-level retry (retry individual failed steps), partial failure recovery (resume from last successful checkpoint), idempotent pipelines (deterministic seeds), and error classification (transient, content policy, quality, fatal).

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

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