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aws/amazon-sagemaker-examples

A collection of Jupyter notebooks demonstrating how to build, train, and deploy machine learning models using Amazon SageMaker and its new Python SDK, SageMaker-Core.

Core Features

Demonstrates end-to-end ML lifecycle with Amazon SageMaker.

Features Jupyter notebooks for practical, guided learning.

Showcases the new SageMaker-Core Python SDK for streamlined ML workflows.

Covers building, training, and deploying various machine learning models.

Optimized for use within Amazon SageMaker Notebook Instances.

Quick Start

Access notebooks via 'SageMaker Examples' tab in Jupyter or SageMaker logo in JupyterLab on an Amazon SageMaker Notebook Instance.

Detailed Introduction

This repository serves as the official collection of Jupyter notebooks designed to guide ML practitioners through the entire machine learning lifecycle using Amazon SageMaker. It provides practical, end-to-end examples covering model building, training, and deployment. Crucially, it introduces and exemplifies the new SageMaker-Core Python SDK, which offers an object-oriented interface, resource chaining, and usability enhancements like type hints and auto-completion. This makes the repository an invaluable resource for learning to leverage SageMaker's powerful, customizable ML infrastructure efficiently on AWS.