AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet.

Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.

A library for training and deploying machine learning models on Amazon SageMaker

ACK service controller for Amazon SageMaker

Probabilistic time series modeling in Python

Adapts algorithms that implement the Grand Challenge inference API for running in SageMaker

It prepares the demo of emotion garden.

Large scale and asynchronous Hyperparameter and Architecture Optimization at your fingertips.

Serve machine learning models within a 🐳 Docker container using 🧠 Amazon SageMaker.

Example notebooks for working with SageMaker Studio Lab. Sign up for an account at the link below!

This is the Docker container based on open source framework XGBoost (https://xgboost.readthedocs.io/en/latest/) to allow customers use their own XGBoost scripts in SageMaker.

Opinionated machine learning with SageMaker

An end-to-end blueprint architecture for real-time fraud detection(leveraging graph database Amazon Neptune) using Amazon SageMaker and Deep Graph Library (DGL) to construct a heterogeneous graph from tabular data and train a Graph Neural Network(GNN) model to detect fraudulent transactions in the IEEE-CIS dataset.

Unlocking Creativity: Generative AI with SageMaker Jumpstart

Tools and IAC to setup SageMaker Studio for team collaboration.

collection of projects computed on AWS

Library for automatic retraining and continual learning

A "Hello world" project on how implement your own ML Model in AWS Sagemaker.

Know How Guide and Hands on Guide for AWS

A curated list of references for Amazon SageMaker

Workshop CDK Template to provision infra for the Deep Visual Search workshop

This project deploys a mobile web app, that uses a machine learning model you train, to detect activities you're doing.

Tools to run Jupyter notebooks as jobs in Amazon SageMaker - ad hoc, on a schedule, or in response to events

Hosting code-server on Amazon SageMaker

Amazon SageMaker Debugger provides functionality to save tensors during training of machine learning jobs and analyze those tensors

Learn how to train and evaluate time series models with the Gluon TS library, and then deploy them for inference as a Multi-Model Server SageMaker endpoint using a custom SageMaker container.

Run DAGs of MLflow Projects in Kubernetes using Concurrent for MLflow

A collection of localized (Korean) AWS AI/ML workshop materials for hands-on labs.