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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, …

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Deploy Amazon SageMaker Autopilot models to serverless inference endpoints

Amazon SageMaker Autopilot automatically builds, trains, and tunes the best machine learning (ML) models based on your data, while allowing you to maintain full control and visibility. Autopilot can also deploy trained models to real-time inference endpoints automatically. If you have workloads with spiky or unpredictable traffic patterns that can tolerate cold starts, then deploying …

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Private Ads Prediction with DP-SGD

Posted by Krishna Giri Narra, Software Engineer, Google, and Chiyuan Zhang, Research Scientist, Google Research Ad technology providers widely use machine learning (ML) models to predict and present users with the most relevant ads, and to measure the effectiveness of those ads. With increasing focus on online privacy, there’s an opportunity to identify ML algorithms …

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Google at EMNLP 2022

Posted by Malaya Jules, Program Manager, Google This week, the premier conference on Empirical Methods in Natural Language Processing (EMNLP 2022) is being held in Abu Dhabi, United Arab Emirates. We are proud to be a Diamond Sponsor of EMNLP 2022, with Google researchers contributing at all levels. This year we are presenting over 50 …