The core of machine learning is mathematical computations that, for most enterprises, are unprecedented in both complexity and volume. These workloads increasingly require high-performance or application-specific infrastructure to ensure AI systems can be built and deployed quickly.
Therefore, the development and deployment of machine learning models requires infrastructure that is fast, scalable, dynamic and cost-effective. But the data scientists building those models have enough on their plate, without the additional cognitive load of managing their infrastructure.
So what are enterprises doing about it? Our 451 Research Voice of the Enterprise AI & Machine Learning Infrastructure 2019 survey, launched in August shows that organizations are already either planning strategies to deal with this (52%) or have one in place (27%). How they go about that differs depending on the size of the organization, available budgets, regulatory status and sometimes geography.
Ad hoc cloud services can help enterprises bridge many of the gaps between these demands and their current infrastructure capabilities. In the future, enterprises will need to adopt more concerted strategies to adapt their IT environments to an era of ubiquitous machine learning. For many organizations, this change will entail adopting new infrastructure technologies designed for AI workloads. For others, it means configuring a network environment capable of applying machine learning models in real-time to make predictions on new data. For all enterprises, these infrastructure changes will require a certain degree of forethought so that IT does not become the barrier to implementation.