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

This article demonstrates how to use Amazon SageMaker Python SDK v3's script mode for training and deploying custom models with decoupled code and containers.

• ModelTrainer and ModelBuilder replace framework-specific classes (SKLearn, PyTorch, XGBoost) with unified APIs
• SourceCode object syncs local code directories into containers at runtime without rebuilding images
• Example 1: Train scikit-learn Random Forest on diabetes dataset and deploy via DJL Serving
• Example 2: Fine-tune Stable Diffusion 3.5 with LoRA using multi-GPU distributed training on ml.g5.12xlarge
• Supports recipe-driven hyperparameters, AWS Secrets Manager integration, and input data channels
• Same API works for tabular ML, generative AI, and distributed training workflows

SDK v3 streamlines ML workflows by decoupling training code from container images, enabling faster iteration and consistent interfaces across diverse model types and scales.