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Image augmentation pipeline for Amazon Lookout for Vision

Amazon Lookout for Vision provides a machine learning (ML)-based anomaly detection service to identify normal images (i.e., images of objects without defects) vs anomalous images (i.e., images of objects with defects), types of anomalies (e.g., missing piece), and the location of these anomalies. Therefore, Lookout for Vision is popular among customers that look for automated …

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Amazon SageMaker JumpStart now offers Amazon Comprehend notebooks for custom classification and custom entity detection

Amazon Comprehend is a natural language processing (NLP) service that uses machine learning (ML) to discover insights from text. Amazon Comprehend provides customized features, custom entity recognition, custom classification, and pre-trained APIs such as key phrase extraction, sentiment analysis, entity recognition, and more so you can easily integrate NLP into your applications. We recently added …

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Building out your support insights pipeline

Getting into the details We wrote previously about how we used clustering to connect requests for support (in text form) to the best tech support articles so we could answer questions faster and more efficiently. In a constantly changing environment (and in a very oddball couple of years) we wanted to make sure we’re focused …

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Kontinuierliches und skalierbares Schwachstellenmanagement bei Palantir

(An English-language version of this post can be read here.) Disclaimer: Bei dem folgendem Blogpost handelt es sich um eine aktualisierte deutsche Version des bereits im Englischen veröffentlichten Blogposts “How Palantir Manages Continuous Vulnerability Scanning At Scale.” Er bezieht sich konkret auf Cloud-Implementierungen von Palantir-Produkten. Bei Kunden, die eine on-prem Installation verwalten, gelten u.U.. andere Prozesse …

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The Ontology: Operating at optimum complexity — as simply as possible

The Ontology: Operating at optimum complexity — as simply as possible Editor’s Note: In this blog post, Senior Director of Enterprise Technology Markus Löffler discusses the principles underlying his belief that the Palantir Foundry Ontology can transform any enterprise. I believe the Ontology is the foundational element for the modern software stack. In this post, I will tell you why. …

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Production Infrastructure at Palantir: User-minded API design

Intro It’s trendy right now for organizations to describe themselves as “outcome-oriented,” where members of the organization are accountable for the results of their work. The Production Infrastructure group at Palantir commits itself to being oriented around our users’ outcomes — but how does this translate into building good software? Our users’ outcomes are often dictated by …

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Formation of Robust Bound States of Interacting Photons

Posted by Alexis Morvan and Trond Andersen, Research Scientists, Google Quantum AI When quantum computers were first proposed, they were hoped to be a way to better understand the quantum world. With a so-called “quantum simulator,” one could engineer a quantum computer to investigate how various quantum phenomena arise, including those that are intractable to …

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Prepare data from Amazon EMR for machine learning using Amazon SageMaker Data Wrangler

Data preparation is a principal component of machine learning (ML) pipelines. In fact, it is estimated that data professionals spend about 80 percent of their time on data preparation. In this intensive competitive market, teams want to analyze data and extract more meaningful insights quickly. Customers are adopting more efficient and visual ways to build …

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Exafunction supports AWS Inferentia to unlock best price performance for machine learning inference

Across all industries, machine learning (ML) models are getting deeper, workflows are getting more complex, and workloads are operating at larger scales. Significant effort and resources are put into making these models more accurate since this investment directly results in better products and experiences. On the other hand, making these models run efficiently in production …

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Damage assessment using Amazon SageMaker geospatial capabilities and custom SageMaker models

In this post, we show how to train, deploy, and predict natural disaster damage with Amazon SageMaker with geospatial capabilities. We use the new SageMaker geospatial capabilities to generate new inference data to test the model. Many government and humanitarian organizations need quick and accurate situational awareness when a disaster strikes. Knowing the severity, cause, …