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

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

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

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

In this AWS Machine Learning Specialty Course, You will gain first-hand experience on how to train, optimize, deploy, and integrate ML in AWS cloud. Learn how to use AWS Built-in SageMaker algorithms and AI, How to Bring Your Own Algorithm, Zero Downtime Model Deployment Options, How to Integrate and Invoke ML from your Application, Automated Hyperparameter Tuning

Repository for training and deploying Generative AI models, including text-text, text-to-image generation and prompt engineering playground using SageMaker Studio.

The projects I do in Machine Learning with PyTorch, keras, Tensorflow, scikit learn and Python.

Build, train & debug, and deploy & monitor with Amazon SageMaker

MLOps workshop with Amazon SageMaker

Safe blue/green deployment of Amazon SageMaker endpoints using AWS CodePipeline, CodeBuild and CodeDeploy.

Deploy FastAI Trained PyTorch Model in TorchServe and Host in Amazon SageMaker Inference Endpoint

This is a short example showing how to utilize Amazon SageMaker's real time endpoints with OpenAI's open source Whisper model for audio transcription.

Compilation of examples of SageMaker inference options and other features.

Deploy Generative AI models from Amazon SageMaker JumpStart using AWS CDK

SageMaker custom deployments made easy

Detect Defects in Products from their Images using Amazon SageMaker

Deploy your AI/ML model to Amazon SageMaker for Real-Time Inference and Batch Transform using your own Docker container image.

Workbench: An easy to use Python API for creating and deploying AWS SageMaker Models

Train and deploy models using TensorFlow 2 with the Object Detection API on Amazon SageMaker

Amazon SageMaker MLOps deployment pipeline for A/B Testing of machine learning models.

End to end Machine Learning with Amazon SageMaker

Deploy Stable Diffusion Model on Amazon SageMaker Endpont

Deploy your pretrained model on AWS SageMaker

MLOps on AWS using Amazon SageMaker Pipelines

From nothing to a deployed object detection model on SageMaker with Detectron2

This workshop will familiarize you with some of the key steps towards building an end-to-end predictive maintenance system leveraging Amazon SageMaker, Amazon Polly and the AWS IoT suite.

This is a demo project showing how to fine-tune and deploy the Whisper model on SageMaker.

Deploying Models to Production with Mlflow and AWS Sagemaker

Use the two different methods (deepspeed and SageMaker model parallelism library) to fine tune llama model on Sagemaker. Then deploy the fine tuned llama on Sagemaker with server side batch.

Tutorial to show how to deploy KoGPT2 model to Amazon SageMaker at scale, and how to fine-tune KoGPT2 model for downstream NLP tasks