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

AWS Deep Learning Containers are pre-built Docker images that make it easier to run popular deep learning frameworks and tools on AWS.

Dispatch and distribute your ML training to "serverless" clusters in Python, like PyTorch for ML infra. Iterable, debuggable, multi-cloud/on-prem, identical across research and production.

すぐに業務活用できるビジネスユースケース集付きの安全な生成AIアプリ実装

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.

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.

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

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

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

A Spark library for Amazon SageMaker.

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

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