Editing Machine Learning at the Edge (section)

==='''Applications for ML at the Edge'''===
The utilization of data to make conjectures about what is going on in the environment and how to respond has a variety of use cases that can greatly benefit people, cities, and the environment. By leveraging and monitoring a constant stream of data and training machine learning models to detect or even respond to different events, there can be many practical applications for such systems. These would rely on the combination of edge devices and machine learning to better enhance experience for users and detect events of interest.

'''Self-Driving Cars''': Imitation learning can be leveraged to better understand and emulate human driving. The low latency that can be provided by edge computing is especially useful for the quick decision making needed by these systems [7].

'''Smart Home Devices''': Understanding user habits by leveraging the available data for them can make smart homes more convenient for users. The increased privacy that can come with edge computing, along with a few extra cybersecurity measures, can ensure the personal data that may be used for training is not compromised.

'''Environmental and Industrial Monitoring:''' Sensors deployed in environments and industrial settings could be trained to recognize when there are anomalies or undesirable behavior, thus ensuring a quick response and active information sending [7].

'''Smart Cities:''' Similar to above, the data collected by sensors in cities can leverage machine learning to help in crime and emergency detection, traffic management, or energy management[7].