AWS Open Source Blog

Category: Amazon Machine Learning

Announcing Amazon CloudWatch for Ray

Amazon CloudWatch is now available for Ray on Amazon Elastic Compute Cloud (Amazon EC2). Ray is an open source (Apache 2.0 License) framework to build and scale distributed applications. CloudWatch is a monitoring and observability service that provides data and actionable insights to monitor your applications, respond to system-wide performance changes, and optimize resource utilization. […]

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Build, train, and deploy Amazon Fraud Detector models using the open source Python SDK

Companies providing digital services are looking for ways to effectively identify fraudulent activities, such as online payment fraud and fake account creation. Amazon Fraud Detector is a fully managed service that uses machine learning (ML) and builds on 20 years of fraud detection expertise from Amazon Web Services (AWS) and Amazon.com to automatically identify potentially […]

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Delta Sharing on AWS

This post was written by Frank Munz, Staff Developer Advocate at Databricks. An introduction to Delta Sharing During the past decade, much thought went into system and application architectures using domain-driven design and microservices, but we are still on the verge of building distributed data meshes. Such data meshes are based on two fundamental principles: […]

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Overview of workflow described in article

Getting started with Feast, an open source feature store running on AWS Managed Services

This post was written by Willem Pienaar, Principal Engineer at Tecton and creator of Feast. Feast is an open source feature store and a fast, convenient way to serve machine learning (ML) features for training and online inference. Feast lets you build point-in-time correct training datasets from feature data, allows you to deploy a production-grade […]

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How to use InfluxDB and Grafana to visualize ML output with AWS IoT Greengrass

Machine learning (ML) algorithms are widely used for computer vision (CV) applications, such as image classification, object detection, and semantic segmentation. With the latest development of the Industrial Internet of Things (IIoT), ML algorithms can be directly implemented at the edge device to process image data and perform anomaly detection, such as for product quality […]

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Creating a bridge between machine learning and quantum computing with PennyLane

In this post, Josh Izaac (Xanadu) and Eric Kessler (AWS) explain how the open source PennyLane project helps bridge the gap between the quantum computing and machine learning communities. Today, we are announcing that AWS is joining the steering council of the PennyLane open source project for variational quantum computing and quantum machine learning. Our […]

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Deploy fast.ai-trained PyTorch model in TorchServe and host in Amazon SageMaker inference endpoint

Over the past few years, fast.ai has become one of the most cutting-edge, open source, deep learning frameworks and the go-to choice for many machine learning use cases based on PyTorch. It has not only democratized deep learning and made it approachable to general audiences, but fast.ai has also become a role model on how […]

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Virtual GPU device plugin for inference workloads in Kubernetes

Machine learning (ML) has become a centerpiece for enterprise transformation. AWS provides a broad and deep set of ML capabilities for builders with all levels of expertise. Developers with no prior ML experience can seamlessly build sophisticated AI-driven applications using AWS AI services. Developers and data scientists can use Amazon SageMaker, a managed machine learning […]

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workflow: how to deploy TorchServe on an Amazon EKS cluster for inference, which will allow you to quickly deploy a pre-trained machine learning model as a scalable, fault-tolerant web-service for low latency inference

Running TorchServe on Amazon Elastic Kubernetes Service

This article was contributed by Josiah Davis, Charles Frenzel, and Chen Wu. TorchServe is a model serving library that makes it easy to deploy and manage PyTorch models at scale in production environments. TorchServe removes the heavy lifting of deploying and serving PyTorch models with Kubernetes. TorchServe is built and maintained by AWS in collaboration […]

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How Amazon retail systems run machine learning predictions with Apache Spark using Deep Java Library

Today more and more companies are taking a personalized approach to content and marketing. For example, retailers are personalizing product recommendations and promotions for customers. An important step toward providing personalized recommendations is to identify a customer’s propensity to take action for a certain category. This propensity is based on a customer’s preferences and past […]

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