Sign up for our newsletter and get the latest big data news and analysis.

insideBIGDATA Guide to Optimized Storage for AI and Deep Learning Workloads – Part 3

Artificial Intelligence (AI) and Deep Learning (DL) represent some of the most demanding workloads in modern computing history as they present unique challenges to compute, storage and network resources. In this technology guide, insideBIGDATA Guide to Optimized Storage for AI and Deep Learning Workloads, we’ll see how traditional file storage technologies and protocols like NFS restrict AI workloads of data, thus reducing the performance of applications and impeding business innovation. A state-of-the-art AI-enabled data center should work to concurrently and efficiently service the entire spectrum of activities involved in DL workflows, including data ingest, data transformation, training, inference, and model evaluation.

insideBIGDATA Guide to Optimized Storage for AI and Deep Learning Workloads – Part 2

Artificial Intelligence (AI) and Deep Learning (DL) represent some of the most demanding workloads in modern computing history as they present unique challenges to compute, storage and network resources. In this technology guide, insideBIGDATA Guide to Optimized Storage for AI and Deep Learning Workloads, we’ll see how traditional file storage technologies and protocols like NFS restrict AI workloads of data, thus reducing the performance of applications and impeding business innovation. A state-of-the-art AI-enabled data center should work to concurrently and efficiently service the entire spectrum of activities involved in DL workflows, including data ingest, data transformation, training, inference, and model evaluation.

DAOS Delivers Exascale Performance Using HPC Storage So Fast It Requires New Units of Measurement

Forget what you previously knew about high-performance storage and file systems. New I/O models for HPC such as Distributed Asynchronous Object Storage (DAOS) have been architected from the ground up to make use of new NVM technologies such as Intel® Optane™ DC Persistent Memory Modules (Intel Optane DCPMMs). With latencies measured in nanoseconds and bandwidth measured in tens of GB/s, new storage devices such as Intel DCPMMs redefine the measures used to describe high-performance nonvolatile storage.

Pure Makes Customers “AI-First” Infrastructure a Reality

Pure Storage (NYSE: PSTG), a fast growing data storage company, announced a host of new and improved AI solutions that provide enterprise customers with the features and functionality needed to execute increasingly complex AI initiatives through any phase or scale. Built on Pure’s industry-leading file and object system, FlashBladeTM, and its joint AI-Ready Infrastructure (AIRITM) offering with NVIDIA, customers can develop and deploy AI rapidly to keep pace with modern business

insideBIGDATA Guide to Optimized Storage for AI and Deep Learning Workloads

Artificial Intelligence (AI) and Deep Learning (DL) represent some of the most demanding workloads in modern computing history as they present unique challenges to compute, storage and network resources. In this technology guide, insideBIGDATA Guide to Optimized Storage for AI and Deep Learning Workloads, we’ll see how traditional file storage technologies and protocols like NFS restrict AI workloads of data, thus reducing the performance of applications and impeding business innovation. A state-of-the-art AI-enabled data center should work to concurrently and efficiently service the entire spectrum of activities involved in DL workflows, including data ingest, data transformation, training, inference, and model evaluation.

insideBIGDATA Guide to Optimized Storage for AI and Deep Learning Workloads

This new technology guide from DDN shows how optimized storage has a unique opportunity to become much more than a siloed repository for the deluge of data constantly generated in today’s hyper-connected world, but rather a platform that shares and delivers data to create competitive business value. The intended audience for this important new technology guide includes enterprise thought leaders (CIOs, director level IT, etc.), along with data scientists and data engineers who are a seeking guidance in terms of infrastructure for AI and DL in terms of specialized hardware. The emphasis of the guide is “real world” applications, workloads, and present day challenges.

When Data-Driven Meets Data Silos: Let the Fun Really Begin

In this special guest feature, Ed Thompson, CTO and co-founder at Matillion, believes that on balance, the systems that lead to having many data silos are a good thing; they indicate a business has the autonomy to choose the best systems in each department. This should make the business more efficient overall. However, the business needs data from all these systems.

Interview: Terry Deem and David Liu at Intel

I recently caught up with Terry Deem, Product Marketing Manager for Data Science, Machine Learning and Intel® Distribution for Python, and David Liu, Software Technical Consultant Engineer for the Intel® Distribution for Python*, both from Intel, to discuss the Intel® Distribution for Python (IDP): targeted classes of developers, use with commonly used Python packages for data science, benchmark comparisons, the solution’s use in scientific computing, and a look to the future with respect to IPD.

NuoDB 4.0 Expands Cloud-native and Cloud-agnostic Capabilities of Distributed SQL Database

NuoDB, the distributed SQL database company, unveiled NuoDB 4.0, featuring expanded cloud-native and cloud-agnostic capabilities with support for Kubernetes Operators and Google Cloud and Azure public clouds. This includes the recently announced Kubernetes Operator to simplify and automate database deployments in Red Hat OpenShift.

Data Lakes: The Future of Data Warehousing?

In this special guest feature, Adwait Joshi, CEO of DataSeers, sees data lakes as a modern take on big data. When you think of a lake, you cannot define its shape and size, nor can you define what lives in it and how. Lakes just form—even if they are man-made, there is still an element of randomness to them and it’s this randomness that helps us in situations where the future is, well, sort of unpredictable.