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

Probabilistic time series modeling in Python

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

A modular and comprehensive solution to deploy a Multi-LLM and Multi-RAG powered chatbot (Amazon Bedrock, Anthropic, HuggingFace, OpenAI, Meta, AI21, Cohere, Mistral) using AWS CDK on AWS

Application implementation with business use cases for safely utilizing generative AI in business operations

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

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

AWS Generative AI CDK Constructs are sample implementations of AWS CDK for common generative AI patterns.

Training deep learning models on AWS and GCP instances

LLMs and Machine Learning done easily

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

Compare MLOps Platforms. Breakdowns of SageMaker, VertexAI, AzureML, Dataiku, Databricks, h2o, kubeflow, mlflow...

Setup end to end demo architecture for predicting fraud events with Machine Learning using Amazon SageMaker

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

A Spark library for Amazon SageMaker.

Library for automatic retraining and continual learning

Toolkit for running TensorFlow training scripts on SageMaker. Dockerfiles used for building SageMaker TensorFlow Containers are at https://github.com/aws/deep-learning-containers.

Amazon SageMaker Local Mode Examples

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

Foundation model benchmarking tool. Run any model on any AWS platform and benchmark for performance across instance type and serving stack options.

This repository contains guidance related to SageMaker AI Projects. SageMaker Projects help organizations set up and standardize developer environments for data scientists and CI/CD systems for MLOps engineers.

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.

Toolkit for running PyTorch training scripts on SageMaker. Dockerfiles used for building SageMaker Pytorch Containers are at https://github.com/aws/deep-learning-containers.

Joining the modern data stack with the modern ML stack

Become a Certified Unicorn Developer and Participant in the API Token Economy

AWS for Bioinformatics Researchers

AWS Data/MLServices sample code & notes for my LinkedIn Learning courses

A curated list of references for Amazon SageMaker

Deep Learning Summer School + Tensorflow + OpenCV cascade training + YOLO + COCO + CycleGAN + AWS EC2 Setup + AWS IoT Project + AWS SageMaker + AWS API Gateway + Raspberry Pi3 Ubuntu Core

SageMakerで機械学習モデルを構築、学習、デプロイする方法が学べるNotebookと教材集