Machine Learning (ML) is traditionally associated with large servers and enormous amounts of computing power. However recent improvements in both the efficiency of the algorithms and processing platforms mean that it is now possible to include ML functionality on much smaller embedded systems.
This article covers some of the advantages and challenges of distributed machine learning applications, discusses some of the suitable ML methodologies for use in an embedded environment, and finally looks at some of the pitfalls for the unwary.
For the last 15 or 20 years Machine Learning has become one of the key building blocks of IT systems and has become part of all of our lives, albeit mostly a hidden one. The ever increasing amounts of available data require increasing sophisticated processing techniques and the advances in ML over this period have been quite mind blowing.