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aws/amazon-sagemaker-examples

A collection of Jupyter notebooks and a new Python SDK demonstrating how to build, train, and deploy machine learning models on Amazon SageMaker.

Core Features

Comprehensive Jupyter notebook examples for ML lifecycle

SageMaker-Core Python SDK for object-oriented SageMaker resource management

Resource chaining for simplified code and parameter handling

Abstracts low-level SageMaker API details and state transitions

Usability improvements: auto-completion, type hints, detailed documentation

Detailed Introduction

This GitHub repository serves as the official collection of example Jupyter notebooks for Amazon SageMaker, showcasing the full breadth of its features for building, training, and deploying machine learning models. It also introduces SageMaker-Core, a new Python SDK designed to streamline interaction with SageMaker resources through an intuitive object-oriented interface. SageMaker-Core simplifies ML workflow management by abstracting complex API details, offering resource chaining, and enhancing developer experience with features like auto-completion and type hints, making it ideal for ML practitioners seeking full customization and efficiency.