Business fine-tuning, when to consider, how to arbitrate

You have an LLM in prod, you pay $4,000/month in tokens, your quality is plateauing. Someone suggests: 'what if we fine-tune a custom model?'. Good idea, or $50K trap? This course arms you to decide like a tech-lead, not like a fan. 5 foundations: (1) when fine-tuning really beats prompting + RAG (and when it fails them), (2) anatomy of a clean dataset (JSONL format, minimum size, curation), (3) concrete Bedrock pipeline (training job, monitoring, deploy with provisioned throughput), (4) rigorous post-FT eval (holdout set, A/B vs baseline, LLM-as-judge), (5) real ROI and prod guard-rails (threshold calc, versioning, drift monitoring). By the end, you know how to say YES or NO to a fine-tuning project in 30 min, and if you say YES, you ship cleanly.

Level : advanced · Duration : 180 min

Modules

1. 1. When to fine-tune, decision tree before blowing $50K
2. 2. Dataset anatomy, format, size, curation
3. 3. AWS Bedrock pipeline, from training job to deploy
4. 4. Rigorous post-fine-tuning eval, prove your model beats its baseline
5. 5. ROI and prod guard-rails, avoid the premature fine-tuning trap

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