AWS Open Source Blog

Category: Learning Levels

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Twin Neural Network Training with PyTorch and and its Deployment with TorchServe on Amazon SageMaker

In this post we demonstrate how to train a Twin Neural Network based on PyTorch and, and deploy it with TorchServe on Amazon SageMaker inference endpoint. For demonstration purposes, we build an interactive web application for users to upload images and make inferences from the trained and deployed model, based on Streamlit, which is an open source framework for data scientists to efficiently create interactive web-based data applications in pure Python.

Apply GitOps to Everything

How to Apply GitOps to Everything Using Amazon Elastic Kubernetes Service (Amazon EKS), Crossplane, and Flux

Open source Crossplane enables GitOps to be applied virtually everywhere using Kubernetes as a proxy to provision and manage cloud resources. This article will take you in a step-by-step workflow to provision Amazon Elastic Kubernetes Service (Amazon EKS) clusters and an Amazon Relational Database Service (Amazon RDS) database the GitOps way using Crossplane and Flux.

How to use Amazon Lookout for Vision Python SDK

Amazon Lookout for Vision Python SDK: Cross-validation and Integration with Other AWS Services

Learn how to use the open source Python SDK for Lookout for Vision in either AWS Glue or AWS Lambda to quickly identify differences in images of objects at scale.

Ogury Uses Open Source Technologies on AWS to Run Low-Latency Machine Learning

This post was contributed by Thomas Ngue Minkeng, Nathalie Au, Marc Bouffard, and Pierre-Marie Airiau from Ogury Ogury, the Personified Advertising company, is using open source machine learning (ML) on AWS to deliver a planned 300,000 inferences per second under 10-ms latency. Ogury’s breakthrough advertising engine delivers precision, sustainability, and privacy protection within one technology […]

Building a multi-tenant Kubeflow environment on Amazon EKS using Amazon Cognito and ADFS

NOTE: Since this blog post was written, much about Kubeflow has changed. While we are leaving it up for historical reference, more accurate information about Kubeflow on AWS can be found here. The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable, and scalable. The project’s goal is […]

Introducing AWS Cloud Map MCS Controller for K8s

Modern applications built using microservices patterns are distributed and dynamic by nature. Deploying these applications to Kubernetes clusters tightly couples the application and cluster together. Increasingly, customers are asking for the ability to deploy applications across clusters to allow for easier upgrades and migrations and to break down isolation boundaries. However, bridging the gap between […]

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Performing canary deployments and metrics-driven rollback with Amazon Managed Service for Prometheus and Flagger

This post was written by Kevin Bell and Stefan Prodan. Canary deployments are a popular tool to reduce risk when deploying software, by exposing a new version to a small subset of traffic before rolling it out more broadly. Creating the machinery to do this kind of controlled rollout, and monitoring for possible problems and […]