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This article discusses TinyML, a sub-branch of machine learning that is concerned with lightweight algorithms capable of running on a device, rather than on a server, and exhibiting very low power consumption and memory overheads. TinyML algorithms offer enhanced integration of existing ML models on memory-restricted devices, and offer confidentiality baked in because the models are run locally and do not require data to be transmitted to third party providers. The adoption of TinyML may be used to mitigate risks associated with the use of machine learning services such as ChatGPT to process sensitive data.
