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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
AWS Deep Learning Containers are pre-built Docker images that make it easier to run popular deep learning frameworks and tools on AWS.
Distribute and run AI workloads magically in Python, like PyTorch for ML infra.
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 Spark library for Amazon SageMaker.
A collection of localized (Korean) AWS AI/ML workshop materials for hands-on labs.
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