DeltaV Virtualization overview

whitePaper | May 4, 2022

The pandemic has brought about significant economic disruption, thereby increasing the need for banks and financial institutions to be more agile. However, Covid-19 is not the only factor that drives automation and digitalization in the credit and lending process in credit institutions. Incumbent banks are increasingly facing pressure from neobanks, these having the strategic advantage of being “born digital”. In addition, customers demand fully digitalized processes with regard to the ease of use and fast turnaround times.

whitePaper | June 24, 2022

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.

whitePaper | August 10, 2020

Digital innovation is happening fast — and there’s no slowing it down. Financial institutions are shifting priorities as third-party apps and services become more prevalent among consumers. Now, staying connected is no longer just an option, it’s critical to achieving success and staying relevant. mx.com | 1 But moving fast isn’t as easy as it sounds. There’s a fine line that financial institutions have to balance when it comes to innovating quickly to give their customers the experiences they’ve come to expect and ensuring their data remains safe and secure. As data sharing and connected integration ecosystems become the new norm, here are three challenges many financial institutions are facing.

whitePaper | July 10, 2020

There’s no arguing that preventing failures and accidents is critical for industry. Unexpected incidents can grind operations to a halt for extended periods of time and necessitate expensive repairs. Just 12 hours of downtime for an oil production platform could cost six to eight million dollars in lost production opportunity alone. A single day of grounding for a plane costs roughly four to five million dollars. Because of these disruptions, industrial sectors are always on the lookout for newer, better maintenance methods, and the approach on everyone’s lips right now is predictive maintenance. While everyone agrees on the name, there is less consensus on what it means or how to implement it. But to truly unlock the potential of predictive maintenance, it needs to be paired with artificial intelligence (AI) and machine learning (ML).

whitePaper | October 18, 2022

Tachyum Prodigy is the industry’s first universal processor, unifying the functionality of CPU, GPU, and TPU into a single monolithic package. Prodigy’s revolutionary new architecture provides 6x more raw performance on AI training and inference workloads than the industry’s highest performing GPU, and up to 10x performance at the same power.

whitePaper | August 25, 2022

Businesses are in different stages with their artificial intelligence (AI) technologies. Even today, after many years of evaluating AI, only one third of organizations have actually put AI into production (31%1 ). Organizations that started somewhat later are currently prototyping their AI solutions (20%). Companies that only recently came on board with AI are experimenting with AI technologies for their business cases (25%). And a relatively large portion (24%) have only just begun evaluating AI for their business cases.