Sources of ML Models

SageMaker AI supports four ways to get a model, from least to most effort:

1. Pre-trained models in SageMaker JumpStart: ready to deploy, or fine-tune and deploy. Least effort and lowest operational overhead.
2. Built-in algorithms: SageMaker's own algorithms, designed to scale to large datasets and significant compute.
3. Script mode with supported frameworks: your own training code on pre-made images for scikit-learn, TensorFlow, PyTorch, MXNet, or Chainer.
4. Custom Docker images: bring your own container with any packages you need. Most effort.

SageMaker JumpStartCopy Anchor Link

• Pre-trained open-source models from popular model hubs for many problem types
• Deploy, fine-tune, and evaluate them, with incremental training before deployment
• Solution templates that set up infrastructure for common use cases
• Runnable example notebooks

Built-in algorithms cover supervised learning (classification and regression), unsupervised learning (clustering, dimension reduction, anomaly detection), image processing, time series, and text analysis.