Preparing data for supervised fine-tuning Part 2: Advanced data strategies
Learning curves, synthetic data mixing, catastrophic forgetting prevention—the engineering playbook for fine-tuning that actually works.
Why it matters
Practitioners building production fine-tuning pipelines need concrete data strategies beyond the basics: learning curve evaluation, high-value subset selection, and synthetic/distilled data blending are table-stakes for avoiding costly training failures.
The key facts
8 to knowTwo-part series on SFT data preparation
Covers learning curves for data readiness evaluation
High-value data subset selection techniques
Synthetic and distilled data augmentation strategies
Multi-source data mixing to prevent catastrophic forgetting
AWS Machine Learning blog (vendor guidance, not product launch)
Learning curves for evaluating data readiness
Part 2 of AWS two-part series on SFT data preparation
Go to the source
AWS Machine Learning Blogaws.amazon.com
Publisher excerpt: The advanced side of supervised fine-tuning data prep. This second post in a two-part series covers evaluating data readiness with learning curves, selecting high-value data subsets, augmenting data with synthetic and distilled examples, and mixing data sources to prevent catastrophic forgetting.