Embedding AI-powered Analytics Into Your Application

whitePaper | December 22, 2022

Machine learning, one of the key building blocks of AI, has been a part of the technological world since the 1950s, when the earliest programmers asked computers to make sense of large sets of data. Programmers have increasingly refined the ability of machines to study data in order to detect patterns that allow computers to then organise information, identify relationships, make predictions and detect anomalies. Today, modern applications of AI have already given us self-driving cars and virtual assistants and have helped us detect fraud and manage resources like electricity more efficiently. Sectors as diverse as retail, sports, banking, manufacturing and healthcare have all found applications for machine learning and AI.

whitePaper | January 15, 2022

An API is a set of routines, protocols and tools for building software applications. They are typically used to expose data or services that can be consumed by other applications. Depending on their purpose, APIs can be used internally, with partners or made available to the general public. This may not sound like something the average consumer cares about, but behind every mobile app and every modern online experience there is at least one API.

whitePaper | April 16, 2020

In construction, we are often designing and constructing made-to-order assets, so there is immediately less certainty. We may be dealing with multiple projects for different people, with different schedules, and run by different project managers. Each will be in a different location, with different site footprints, different ground conditions, different access routes, etc. And, partly influenced by their locations, we are often dealing with different suppliers we might select local designers, subcontractors, plant, materials, and product suppliers based on their proximity to the site.

whitePaper | July 15, 2022

Today, the use cases for AI (artificial intelligence) and machine learning are developing at a remarkable pace. We are beginning to see cases where AI is contributing to society through its implementation in systems and services that are in production and consumer-facing. These include the automation of nuanced decisions that until now could only be made by humans, thereby increasing the profitability of corporations, and increasing customer satisfaction through the personalization of services making them tailored for each user. In addition, important discussions have begun on the adverse effects such AI systems have on humans and the resulting ethical issues they can create.

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 | September 23, 2022

It’s been several years since organizations began adopting artificial intelligence (AI) to improve their business; few have come close to mastering its existing capabilities. A small number of organizations in our research aim to become AI-driven—a status we define as AI and machine learning underpinning almost everything the enterprise does—by 2025. However, this elite group—who we term “AI leaders”1—as well as the many others looking simply to embed AI more firmly in the enterprise foundations face formidable challenges to achieving their objectives.