5 Data Visualization Pitfalls and How to Avoid Them

whitePaper | August 26, 2022

Evolving data centers into intelligent hubs for digitized business processes poses numerous challenges for IT managers. Many organizations are already adopting hybrid IT concepts to make their data center operations more flexible. However, they also have to keep an eye on new requirements such as IoT, AI, blockchain, container technology as well as data security, legal regulations and climate change. How can all this be seamlessly integrated and managed? And, what will matter next for the data center of the future?

whitePaper | March 30, 2022

This paper presents data from a large-scale study on how code quality impacts software companies in terms of time-to-market and product experience. We conclude with an analysis of the impact and specific recommendations towards successful software development.

whitePaper | July 21, 2020

Back in 2016, the most popular app download on Christmas Day was Fitbit, dominating Apple’s App Store. The rapid adoption of Fitbit and other wearable fitness trackers has underpinnings in psychological motivation theories. Realtime data monitoring provides a powerful incentive that is capable of changing human behavior. Since 2016, the wearable technology market has continued to evolve, including the acquisition of Fitbit by Google, which was announced in November 2019.

whitePaper | February 16, 2023

This technical white paper discusses the market trends, use cases, and benefits of Conversational AI. It describes a solution and validated reference architecture for Conversational AI with the Kore.ai Experience Optimization Platform on Dell infrastructure.

whitePaper | January 11, 2023

Improving the trustworthiness of artificial intelligence (AI) systems is a shared priority for the private and public sectors, as indicated by prolific research and guidelines in recent years.

whitePaper | January 20, 2022

Machine learning (ML) algorithms are moving to the IoT edge due to various considerations such as latency, power consumption, cost, network bandwidth, reliability, privacy and security. Hence, there is an increasing interest in developing neural network (NN) solutions to deploy them on low-power edge devices such as the Arm Cortex-M microcontroller systems. To enable that, we present CMSIS-NN, an open-source library of optimized software kernels that maximize the NN performance on Cortex-M cores with minimal memory footprint overhead. We further present methods for NN architecture exploration, using image classification on CIFAR-10 dataset as an example, to develop models that fit on such constrained devices.