Editing Machine Learning at the Edge (section)

==='''Introduction'''===
Machine Learning is a branch of Artificial Intelligence that is primarily concerned with the training of algorithms to look at sets of data, analyze patterns, and generate conclusions so that the gathered data can be used to generate results and carry out tasks. As an application, machine learning is especially relevant when considering edge computing [7]. Given the recent prevalence and rise in the field of machine learning, it is inevitable that deployment of machine learning algorithms will play a crucial role in the function of edge computing systems as well. The abundant sensor networks often involved in edge systems provide a means of gathering very large amounts of data. With the help of machine learning, such data can be utilized in very effective ways and automation methods can be employed to improve efficiency. Machine learning on edge devices themselves can help ease the burden on cloud systems and the networks connected to them as well, especially if the model does need such large computational power such as in the case of certain Small Language Models (SLMs). However, the main issue regarding machine learning at the edge lies in the devices, which may have limited computational power, and the environment, which may change dynamically and be effected by network congestion, device outages, or other unexpected events. If these challenges can be overcome, edge systems could play a pivotal role in advancing machine learning and utilize it to maximize efficiency.

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