Enterprises moving their artificial intelligence projects into full scale development are discovering escalating costs based on initial infrastructure choices. Many companies whose AI model training infrastructure is not proximal to their data lake incur steeper costs as the data sets grow larger and AI models become more complex.
The traditional approach for artificial intelligence (AI) and deep learning projects has been to deploy them in the cloud. Because it’s common for enterprise software development to leverage cloud environments, many IT groups assume that this infrastructure approach will succeed as well for AI model training.
As more companies deploy artificial intelligence (AI) initiatives to help transform their businesses, key areas where projects can go off the rails are becoming clear. Many problems can be avoided with some advanced planning, but several hidden obstacles exist that companies don’t often see until it’s too late.
Protecting remote workers’ access to cloud applications, public cloud environments, and private access networks is crucial. While organizations are thriving in highly collaborative environments with globally dispersed teams, partners, vendors, and suppliers, the sharing of data comes with risk. It is now more imperative than ever to have a precise level of control and insight into the data sharing process.
The need of the hour is to ‘converge’ critical processes, information flows and technologies across the organization, supply chain and extended ecosystem. Explore the NC State University research to learn more.