Phase 7: Multi-Model Pipeline Orchestration · 50 min · Python · Celery · Redis
A/B Testing Models & Batch Processing
Same prompt, multiple models, compare quality — that's A/B testing. 1000 products overnight — that's batch processing.
Real-world application: A/B testing and batch processing at scale are production engineering skills that demonstrate you can operate generative media systems, not just build them.
What you will learn
- Implement A/B testing models: same prompt, multiple models, compare quality scores
- Implement A/B testing parameters: same model, different guidance/steps/samplers
- Build batch processing: 1000 products through image pipeline overnight
- Implement batch orchestration: job queuing, progress tracking, failure handling, resume
Objective
Compose media stages with explicit state, routing, retry, cost, and quality boundaries. In the paired studio lab, build a batch processing system that takes a list of 20+ product descriptions, generates an image for each using FLUX via fal.ai, tracks progress, handles failures with retry, and produces a final report with success/failure counts, total cost, and average generation time. Use Celery + Redis for job queuing.
The Problem
A team needs to:
- A/B test models: Is FLUX better than SDXL for product images? Same prompt, both models, compare quality scores
- A/B test parameters: Does 30 steps produce better results than 20 steps? Same model, different parameters
- Batch process: Generate images for 1000 products overnight — queue, track progress, handle failures, resume
These are production operations skills — running and operating generative media systems at scale.
What you'll build
Implement A/B testing for models (same prompt, different models, compare scores) and parameters (same model, different seeds/steps/cfg). Build batch processing with job queuing, progress tracking, failure handling, and resume.
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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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