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

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

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

Fast, Pythonic AI services and workflows on your own infra. Unobtrusive, debuggable, PyTorch-like APIs.

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

Training deep learning models on AWS and GCP instances

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

LLMs and Machine Learning done easily

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

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

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

A Spark library for Amazon SageMaker.

Generative AI を活用したビジネスユースケースのデモンストレーション

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

Library for automatic retraining and continual learning

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

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

Amazon SageMaker Local Mode Examples