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How ML-fueled recommendations help developers optimize security, price-performance, and carbon reduction

There’s a lot of talk about the positive impact that machine learning can have on our lives as citizens and consumers. But did you know that it can reduce complexity and toil for cloud administrators? Google Cloud Active Assist uses data, intelligence, and ML to optimize the security, performance, and cost of your cloud environment. …

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Run Google Cloud Speech AI locally, no internet connection required

We’ve all been there— asking a voice assistant to play a song, launch an app, or answer a question, but the assistant doesn’t comply. Maybe it’s a network outage, or maybe you’re in the middle of nowhere, far away from coverage—either way the result is the same: the voice assistant can’t connect to the server …

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Más que anonimizar (Explicando Palantir, #3)

(An English-language version of this post can be read here.) Nota del editor: Este es el tercer post de Palantir Explained, una serie que explora una selección de temas, incluido nuestro enfoque hacia la privacidad, la seguridad, y la seguridad de la IA/ML, entre otros. Las entradas anteriores exploran nuestro modelo de negocio y los controles …

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Do Modern ImageNet Classifiers Accurately Predict Perceptual Similarity?

Posted by Manoj Kumar, Research Engineer, and Ekin Dogus Cubuk, Research Scientist, Google Research The task of determining the similarity between images is an open problem in computer vision and is crucial for evaluating the realism of machine-generated images. Though there are a number of straightforward methods of estimating image similarity (e.g., low-level metrics that …

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Detect fraudulent transactions using machine learning with Amazon SageMaker

Businesses can lose billions of dollars each year due to malicious users and fraudulent transactions. As more and more business operations move online, fraud and abuses in online systems are also on the rise. To combat online fraud, many businesses have been using rule-based fraud detection systems. However, traditional fraud detection systems rely on a …

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Implement RStudio on your AWS environment and access your data lake using AWS Lake Formation permissions

R is a popular analytic programming language used by data scientists and analysts to perform data processing, conduct statistical analyses, create data visualizations, and build machine learning (ML) models. RStudio, the integrated development environment for R, provides open-source tools and enterprise-ready professional software for teams to develop and share their work across their organization Building, …

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Design patterns for serial inference on Amazon SageMaker

As machine learning (ML) goes mainstream and gains wider adoption, ML-powered applications are becoming increasingly common to solve a range of complex business problems. The solution to these complex business problems often requires using multiple ML models. These models can be sequentially combined to perform various tasks, such as preprocessing, data transformation, model selection, inference …

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Run interactive pipelines at scale using Beam Notebooks

To all Apache Beam and Dataflow users: If you’ve experimented with Beam, prototyped a pipeline, or verified assumptions about a dataset, you might have used Beam Notebooks or other interactive alternatives such as Google Colab or Jupyter Notebooks. You might also have noticed a gap between running a small prototype pipeline in a notebook and …

How Tarteel Uses AI to Help Arabic Learners Perfect Their Pronunciation

There are some 1.8 billion Muslims, but only 16% or so of them speak Arabic, the language of the Quran. This is in part due to the fact that many Muslims struggle to find qualified instructors to give them feedback on their Quran recitation. Enter today’s guest and his company Tarteel, a member of the …

The Calibration Generalization Gap

This paper was accepted at the Workshop on Distribution-Free Uncertainty Quantification at ICML 2022. Calibration is a fundamental property of a good predictive model: it requires that the model predicts correctly in proportion to its confidence. Modern neural networks, however, provide no strong guarantees on their calibration— and can be either poorly calibrated or well-calibrated …