Hewlett-Packard Company

whitePaper | June 22, 2022

Artificial intelligence (AI) and machine learning have revolutionized how organizations are using their data. Automated machine learning (AutoML) facilitates and improves the endto-end data science process. This process includes everything from preprocessing and cleaning the data, selecting and engineering appropriate features, tuning and optimizing the model, analyzing results, explaining and documenting the model, and of course, deploying it into production.

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 | July 23, 2020

IT Operations Management (ITOM) is more important than ever in today’s fast-changing world. It helps IT to ensure availability, maintenance and deployment of hardware, software, services and processes. By providing IT with a single platform for operations management, ITOM is the enabler of speed, agility, complete visibility and rapid problem detection. This maximizes business return from IT investment by cost-effectively achieving optimum IT performance.

whitePaper | July 28, 2022

Before the pandemic, the path to AI adoption used to be slow and steady. According to the survey of industryleading firms, only 48% of companies deemed AI and Big Data a source of innovation. In 2021, this number has climbed to almost 65 percent.

whitePaper | July 28, 2022

Many businesses have recently evolved to support large remote or hybrid workforces. This initial transition was imagined to be temporary, relying on home networks to provide the connectivity needed for work. Now, two years later, companies are contemplating technology improvements to address a more permanent remote workforce.

whitePaper | May 15, 2023

The future of AI is hybrid. As generative AI adoption grows at record-setting speeds1 and drives higher demand for compute,2 AI processing must be distributed between the cloud and devices for AI to scale and reach its full potential – just like traditional computing evolved from mainframes and thin clients to today’s mix of cloud and edge devices. A hybrid AI architecture distributes and coordinates AI workloads among cloud and edge devices, rather than processing in the cloud alone. The cloud and edge devices, such as smartphones, vehicles, PCs, and IoT devices, work together to deliver more powerful, efficient, and highly optimized AI.