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.
Many enterprises around the world are discovering new insights, revenue and efficiencies through the use of artificial intelligence (AI). At the same time, companies are discovering that they can accelerate their projects by adjusting their infrastructure approach. These changes have helped to create new opportunities and growth options, as well as preventing a trip to the pile of AI failures.
Even with the best planning, companies should be ready to adjust projects when faced with unexpected challenges or as parameters change due to business needs. This is no difference in the development of AI models, where initial results may lead teams down a new path that requires a rethinking of their AI infrastructure.
The use of artificial intelligence (AI) continues to transform enterprises by creating new products, boosting revenues, cutting costs and increasing efficiency. But getting to those successful implementations has been tricky for some organizations, given the complexity of the technology and potential for a high failure rate for those who jump in without a plan.