A small collection of custom kernels for running Sagemaker Notebooks an Training Jobs

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

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.

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

Proyecto final de la asignatura de Arquitecturas Empresariales.

A Python-based library for automating the migration of EFS storage from one SageMaker Studio domain to another using AWS DataSync

SageMaker Experiments and DVC

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.

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

A repo for creating Sagemaker jobs

The primary objective of this project was to build and deploy an image classification model for Scones Unlimited, a scone-delivery-focused logistic company, using AWS SageMaker.

Image Classifiers are used in the field of computer vision to identify the content of an image and it is used across a broad variety of industries, from advanced technologies like autonomous vehicles and augmented reality, to eCommerce platforms, and even in diagnostic medicine.

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

Amazon SageMaker training jobs using Snowpark Python API

Workshop for running HuggingFace Models on Amazon SageMaker.

Training different models for Predicting Bike Sharing Demand by using AutoGluon's TabularPredictor.fit() on AWS SageMaker Studio

A search application using Aurora Postgresql and pgvector for an online retail store product catalog

AWS SageMaker를 이용한 MLOps와 LLMOps