AWS Open Source Blog

Category: Artificial Intelligence

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.

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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 […]

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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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Using Apollo Server on AWS Lambda with Amazon EventBridge for real-time, event-driven streaming

GraphQL is an application-level query language that helps clients and servers communicate by establishing a common protocol for queries. It represents an alternative to the REST style: unlike REST, GraphQL gives the client, not the server, the power to define what kind of data will be included in the response to its query. GraphQL allows […]

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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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Implementing a hub and spoke dashboard for multi-account data science projects

Modern data science environments often involve many independent projects, each spanning multiple accounts. In order to maintain a global overview of the activities within the projects, a mechanism to collect data from the different accounts into a central one is crucial. In this post, we show how to leverage existing services—Amazon DynamoDB, AWS Lambda, Amazon […]

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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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Solving the Traveling Salesperson Problem with deep reinforcement learning on Amazon SageMaker

The Traveling Salesperson Problem (TSP) is one of the most popular NP-hard combinatorial problems in the theoretical computer science and operations research (OR) community. It asks the following question: “Given a list of cities and the distances between each pair of cities, what is the shortest possible route that visits each city exactly once and […]

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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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