Best Practices in Securing a Multicloud World

whitePaper | August 25, 2022

The document focuses on the best practices to consider for API design by breaking it into its basic building blocks. Along with each recommendation the document also describes the reason and impact of a certain design decision through scenarios. The document provides recommendations on object representations, usage of headers and URL/URI templates to consider.

whitePaper | December 29, 2022

In our modern world, taking advantage of Artificial Intelligence (AI) to gain insights from data is becoming more prevalent day by day. Graphical Processing Unit (GPU) systems use multiple cores to perform parallel processing, running select workloads to decrease processing times. Compared to GPUs, Central Processing Units (CPUs) have fewer cores; previously, this resulted in less capacity for parallelized processing. To move beyond this limitation, Intel has released new hardware that runs typical AI mathematical computations more efficiently on the CPU, and has also released libraries with hardware optimizations that enable an additional increase in performance.

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 12, 2020

“The services market for artificial intelligence technologies is rapidly maturing on the back of several years of learning and a realization—delivering AI is unlike delivering any other technology thus far. Delivering on the promise of AI calls for far more collaboration between service providers, tech vendors, and enterprise clients.” The biggest differentiator in enterprise AI services, as described by most of the customer leaders, is the quality of people—not just the quantity available for a specific skill. Domain understanding and data engineering capabilities are top priorities. A culture of innovation, experimentation, collaboration, and co creation with customers is the key winning formula here.”

whitePaper | May 20, 2021

AI has become a strategic imperative. Early adopters are achieving massive returns, while others risk falling behind. Yet, with AI knowledge and resources scarce, most companies cannot copy the FAANG approach. Even if their vast resources were more readily available, it’s hard to reboot an organization without disrupting the bottom line. Most executives need to work with the resources available — their existing people, processes, and technology.

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.