Editing Machine Learning at the Edge (section)

==='''Methods of Data Optimization'''===

'''Data Selection:''' Certain types of data may be more useful than others, and therefore the ones that most affect the accuracy of a model can be offloaded to edge devices. This decreases the workload on them because less data must be processed, while also conserving the accuracy of the model as much as possible. Some larger data may not be needed, such as only putting Small Language Models on edge devices that can handle simple commands and prompts, rather than having to offload an entire LLM onto the device, which may overload its computational abilities and not provide enough use.

'''Data Compression:''' Data can be compressed to a smaller form in order to fit the constraints of edge devices. This is especially true given their limited memory, and also makes the workload smaller. Quantization, discussed previously, is a prevalent example of this.

'''Container Workloads:''' Container workloads can be very useful, as they provide all resources and important data the device needs for processing the work. By examining the computational abilities of the device, different sized workloads can be allocated as deemed necessary to maximize the efficiency of the training.

'''Batch size tuning:''' The batch size used by a certain model is very important when considering the memory constraints of edge devices. Batch sizes that are smaller allow for a quicker and more efficient means of training models, and are less likely to lead to bottlenecks in memory. This is related to partitioning because the computing and memory capacity of the devices available are very important factors to consider.