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Preparing data for supervised fine-tuning Part 2: Advanced data strategies

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…

  • Assess dataset quality for effective fine-tuning.
  • Perform learning curve analysis to determine optimal dataset size.
  • Use data selection techniques to identify high-quality subset.

Bring your own model with Amazon SageMaker AI: Script mode in SDK v3

The SageMaker Python SDK v3 redesigns script mode with unified ModelTrainer and ModelBuilder classes. This post walks through two end-to-end examples, a scikit-learn Random Forest and a multi-GPU…

  • Amazon SageMaker AI introduces Script mode in SDK v3 for bringing own models.
  • ModelTrainer and ModelBuilder replace v2 Estimator family and Model/Predictor pattern.
  • SourceCode object configures sourcedir and command for training and inference.

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