Tag: ai/ml

The Challenge Effective vulnerability management is a cornerstone of any established security program. For complex cloud software providers like Palantir, staying on top of vulnerabilities and quickly remediating them is critical to staying ahead of our adversaries. If undetected or unmitigated, vulnerabilities in container images and software dependencies can rapidly become a blind spot that …

Read more “How Palantir Manages Continuous Vulnerability Scanning at Scale”

Today, we’re excited to announce self-service quota management support for Amazon Textract via the AWS Service Quotas console, and higher default service quotas in select AWS Regions. Customers tell us they need quick turnaround times to process their requests for quota increases and visibility into their service quotas so they may continue to scale their …

Read more “Introducing self-service quota management and higher default service quotas for Amazon Textract”

Editor’s note: This blog post is the third in a series about Palantir Apollo, following publication of Why Traditional Approaches to Continuous Deployment Don’t Work Today and Palantir Apollo Orchestration: Constraint-Based Continuous Deployment For Modern Architectures. Over the last decade, infrastructure platforms have grown to meet the increasing demand for using containers as the fundamental …

Read more “How Palantir Apollo Saves Developer Time on Kubernetes”

Un enfoque íntegro de la privacidad y la gobernanza Editor’s Note: This is a lightly edited translation of the original English-language post. Palantir Foundry es una plataforma de software que permite a nuestros clientes sincronizar sus datos en un entorno seguro en el que todos los miembros de la organización pueden utilizar dichos datos para …

Read more “Protección de Datos en Palantir Foundry”

This post is co-written by Goktug Cinar, Michael Binder, and Adrian Horvath from Bosch Center for Artificial Intelligence (BCAI). Revenue forecasting is a challenging yet crucial task for strategic business decisions and fiscal planning in most organizations. Often, revenue forecasting is manually performed by financial analysts and is both time consuming and subjective. Such manual …

Read more “Large-scale revenue forecasting at Bosch with Amazon Forecast and Amazon SageMaker custom models”

Increasingly more enterprises adopt Machine Learning (ML) capabilities to enhance their services, products, and operations. As their ML capabilities mature, they build centralized ML Platforms to serve many teams and users across their organization. Machine learning is inherently an experimental process requiring repeated iterations. An ML Platform standardizes the model development and deployment workflow to …

Read more “Building a Machine Learning Platform with Kubeflow and Ray on Google Kubernetes Engine”

Posted by Jeremy Maitin-Shepard and Laramie Leavitt, Software Engineers, Connectomics at Google Many exciting contemporary applications of computer science and machine learning (ML) manipulate multidimensional datasets that span a single large coordinate system, for example, weather modeling from atmospheric measurements over a spatial grid or medical imaging predictions from multi-channel image intensity values in a …

Read more “TensorStore for High-Performance, Scalable Array Storage”