What are Machine Learning Solutions for AI?
A machine learning (ML) solution is a complete set of intellectual property, tools, and software for AI development across a vast array of devices. A complete ML solution can power all types of ML required for AI, including supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.
Fundamentally, ML is about allowing different systems to learn from data and make decisions or produce other outcomes based on inputs. ML solutions can quickly become fragmented or outdated given the pace of innovation, and might work well for one very specific device or environment, but not for others, creating immense complexity.
A sustainable ML solution enables AI development that is based on a common software framework. It is also scalable, flexible, and power efficient across heterogeneous cloud and edge computing environments.
Why Do Machine Learning Solutions Matter?
Complete ML solutions that start at the device level are essential because the “machine” in ML is increasingly variable. ML must be enabled everywhere: from a centralized datacenter to the outermost edges of a network, from smartphones and fitness devices to heavy equipment and predictive maintenance sensors. ML solutions make development efforts repeatable and sustainable across diverse ecosystems and uses—for instance, data processing on low-power devices in an IoT or edge environment.