Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
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Updated
Jun 11, 2024 - Jupyter Notebook
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, Mistral) using AWS CDK on AWS
Write local debuggable Python which traverses your powerful remote infra. Deploy as-is. Unobtrusive, unopinionated, 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.
AWS Generative AI CDK Constructs are sample implementations of AWS CDK for common generative AI patterns.
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.
Setup end to end demo architecture for predicting fraud events with Machine Learning using Amazon SageMaker
Amazon SageMaker Local Mode Examples
Add a description, image, and links to the sagemaker topic page so that developers can more easily learn about it.
To associate your repository with the sagemaker topic, visit your repo's landing page and select "manage topics."