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(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 …

Read more “Más que anonimizar (Explicando Palantir, #3)”

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 …

Read more “Do Modern ImageNet Classifiers Accurately Predict Perceptual Similarity?”

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 …

Read more “Detect fraudulent transactions using machine learning with Amazon SageMaker”

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, …

Read more “Implement RStudio on your AWS environment and access your data lake using AWS Lake Formation permissions”

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 …

Read more “Design patterns for serial inference on Amazon SageMaker”

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 …

Read more “Run interactive pipelines at scale using Beam Notebooks”