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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
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.
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.
Training deep learning models on AWS and GCP instances
AWS Generative AI CDK Constructs are sample implementations of AWS CDK for common generative AI patterns.
LLMs and Machine Learning done easily
Large scale and asynchronous Hyperparameter and Architecture Optimization at your fingertips.
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.
Library for automatic retraining and continual learning
A collection of localized (Korean) AWS AI/ML workshop materials for hands-on labs.
Toolkit for running TensorFlow training scripts on SageMaker. Dockerfiles used for building SageMaker TensorFlow Containers are at https://github.com/aws/deep-learning-containers.