Python SDK, Proxy Server (AI Gateway) to call 100+ LLM APIs in OpenAI (or native) format, with cost tracking, guardrails, loadbalancing and logging. [Bedrock, Azure, OpenAI, VertexAI, Cohere, Anthropic, Sagemaker, HuggingFace, VLLM, NVIDIA NIM]

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

Probabilistic time series modeling in Python

AI and Machine Learning with Kubeflow, Amazon EKS, and SageMaker

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

Provide JSON file template that demonstrate how to create customize Well-Architected reviews using Custom lenses.

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

DeepRacer workshop content. This Guidance demonstrates how software developers can use an Amazon SageMaker Notebook instance to directly train and evaluate AWS DeepRacer models with full control

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.

Neo-AI-DLR is a common runtime for machine learning models compiled by AWS SageMaker Neo, TVM, or TreeLite.

Training deep learning models on AWS and GCP instances

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Code and associated files for the deploying ML models within AWS SageMaker

LLMs and Machine Learning done easily

A collection of sample scripts to customize Amazon SageMaker Notebook Instances using Lifecycle Configurations

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

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

Amazon SageMaker workshops: Introduction, TensorFlow in SageMaker, and more

Case studies, examples, and exercises for learning to deploy ML models using AWS SageMaker.

Machine Learning Ops Workshop with SageMaker: lab guides and materials.

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

Workshop content for applying DevOps practices to Machine Learning workloads using Amazon SageMaker

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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.