A helper library to connect into Amazon SageMaker with AWS Systems Manager and SSH (Secure Shell)

This repository contains examples of Docker images that can be used as custom images for KernelGateway Apps in SageMaker Studio

MLOps workshop with Amazon SageMaker

使用SageMaker+XGBoost，将时间序列转换为监督学习，完成预测性维护的实践

CLI for building Docker images in SageMaker Studio using AWS CodeBuild.

Hosting code-server on Amazon SageMaker

AWS SageMaker를 이용한 MLOps와 LLMOps

Tools and IAC to setup SageMaker Studio for team collaboration.

SageMaker Experiments and DVC

Sample datasets and code for operationalizing Amazon Fraud Detector using SageMaker DataWrangler, Feature Store, and Pipelines.

SageMaker Studio Docker CLI Extension

Sagemaker Studio Docker UI Extension

This solution provides a way to deploy SageMaker Studio in a private and secure environment. The solution integrates with a Custom SAML 2.0 Application as the mechanism to trigger the authentication to Amazon SageMaker Studio with the ability to limit the authorization to specific network environments.

This is an example to demonstrate Amazon SageMaker Data Wrangler capabilities. The workshop showcases entire ML workflow steps for Diabetic Patient Readmission Dataset from UCI.

This solution shows how to deliver reusable and self-contained custom components to Amazon SageMaker environment using AWS Service Catalog, AWS CloudFormation, SageMaker Projects and SageMaker Pipelines.

This repository contains samples for fine-tuning embedding models using Amazon SageMaker. Embedding models are useful for tasks such as semantic similarity, text clustering, and information retrieval. Fine-tuning these models on your specific domain data can greatly improve their performance.

Workshop for running HuggingFace Models on Amazon SageMaker.

Amazon SageMaker training jobs using Snowpark Python API