Stars
A collection of recommended practices to accelerate the building of secure data science environments in regulated environments.
Models and examples built with TensorFlow
Use Amazon SageMaker and Deep Graph Library (DGL) for Fraud Detection in Networks
Using Deep Learning for Demand Forecasting with Amazon SageMaker
A game theoretic approach to explain the output of any machine learning model.
Amazon SageMaker Solution for explaining credit decisions.
Repo to demonstrate conversion from PyTorch to TensorFlow
Detect whether a person is wearing a mask or not
Toolkit for allowing inference and serving with PyTorch on SageMaker. Dockerfiles used for building SageMaker Pytorch Containers are at https://github.com/aws/deep-learning-containers.
Example of integrating & using Amazon Textract, Amazon Comprehend, Amazon Comprehend Medical, Amazon Kendra to automate the processing of documents for use cases such as enterprise search and disco…
This workshop demonstrates how to build a Document parser and query engine with Amazon Textract and other services, such as ElasticSearch and DynamoDB.
Build end-to-end Machine Learning pipeline to predict accessibility of playgrounds in NYC
Step Functions Data Science SDK for building machine learning (ML) workflows and pipelines on AWS
Code for the book Deep Learning with PyTorch by Eli Stevens, Luca Antiga, and Thomas Viehmann.
Examples showing use of NGC containers and models withing Amazon SageMaker
Multi Model Server is a tool for serving neural net models for inference
A library for training and deploying machine learning models on Amazon SageMaker
Serve, optimize and scale PyTorch models in production
Repository with torchserve examples
Train machine learning models within a 🐳 Docker container using 🧠 Amazon SageMaker.
Custom docker container for Catboost on Amazon SageMaker
Serve machine learning models within a 🐳 Docker container using 🧠 Amazon SageMaker.
MLOps with Amazon Sagemaker and Amazon CodePipeline with multiple AWS accounts