Scaling AI inManufacturing Operations:A Practitioners’ Perspective

whitePaper | August 30, 2022

Traditional database management systems have become more essential than ever, as data volume, velocity, and variety all increase at breakneck speed. But the harsh truth is that legacy database management systems were never designed for a digital-first business climate shaped by such factors as cloud computing, pervasive mobility, microservices, new data sources, and cloud-native application development.

whitePaper | November 30, 2022

The first-generation low-code platforms required considerable adaptations and months of development in the late 1990s and early 2000s. Gartner expects that by 2024, low-code/no-code automation will account for 65% of all app development.

whitePaper | June 30, 2022

Low code and no code (LCNC) development platforms expedite the creation of new applications with minimal or no coding requirements, as well as offer tools for non-programmers to create applications of their own. Low code development was introduced in the early 2000s to increase developer productivity. It uses a variety of approaches that automate and abstract application development activities, such as drag and drop editors, code generation, component assembly and model-driven development.

whitePaper | November 23, 2022

The modern distributed workplace—including enterprises with thousands of branches and remote workers—requires updated approaches to cloud, networking, and security infrastructure. Cloud migration has opened the door to a huge increase in sophisticated applications, requiring networks that can carry traffic to an increasingly remote workforce. In turn, this creates a unique set of new requirements for security, including special capabilities to handle unpredictable locations of users, applications, and traffic flows.

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 | January 5, 2020

Machine learning (ML) is an application of artificial intelligence (AI) that allows systems to automatically learn and improve from exposure to more data without being explicitly programmed. ML focuses on the development of computer programs that can access data and use it to learn for themselves.1 While AI represents the broader concept of machines being able to carry out tasks in an intelligent way, machine learning is a current application of AI based on the idea that we can give machines access to data, and they can use that data to learn for themselves.