Giuseppe Angelo Porcelli's repositories
end-to-end-ml-sm
End to end Machine Learning with Amazon SageMaker
sagemaker-custom-training-containers
Implementations of Amazon SageMaker-compatible custom containers for training.
smlambdaworkshop
Building Your Own ML Application with AWS Lambda and Amazon SageMaker
end-to-end-ml-application
Build your own Machine Learning application with Amazon SageMaker, AWS Glue and Amazon API Gateway
sagemaker-custom-serving-containers
Custom serving containers for Amazon SageMaker
huggingface-deploy-pytorch-sagemaker
Examples on how to deploy HF PyTorch models to Amazon SageMaker
sagemaker-custom-studio-containers
Custom container implementations for Amazon SageMaker Studio
amazon-sagemaker-build-train-deploy
End-to-End Machine Learning with Amazon SageMaker
amazon-sagemaker-codeserver
Hosting code-server on Amazon SageMaker
amazon-sagemaker-examples
Example notebooks that show how to apply machine learning, deep learning and reinforcement learning in Amazon SageMaker
amazon-sagemaker-notebook-instance-lifecycle-config-samples
A collection of sample scripts to customize Amazon SageMaker Notebook Instances using Lifecycle Configurations
awsome-distributed-training
Collection of best practices, reference architectures, model training examples and utilities to train large models on AWS.
sagemaker-mxnet-container
This support code is used for making the MXNet framework run on Amazon SageMaker.
sagemaker-python-sdk
A library for training and deploying machine learning models on Amazon SageMaker
sagemaker-safe-deployment-pipeline
Safe deployment pipeline for Amazon SageMaker
sagemaker-spark
Examples of running Spark code in SM
sagemaker-spark-container
The SageMaker Spark Container is a Docker image used to run data processing workloads with the Spark framework on Amazon SageMaker.
sagemaker-wandb
SageMaker and W&B
sm-workshop
Amazon SageMaker Workshop
StableStudio
Community interface for generative AI