  In this current technological world, the application of machine learning is
becoming ubiquitous. Incorporating machine learning algorithms on extremely
low-power and inexpensive embedded devices at the edge level is now possible
due to the combination of the Internet of Things (IoT) and edge computing. To
estimate an outcome, traditional machine learning demands vast amounts of
resources. The TinyML concept for embedded machine learning attempts to push
such diversity from usual high-end approaches to low-end applications. TinyML
is a rapidly expanding interdisciplinary topic at the convergence of machine
learning, software, and hardware centered on deploying deep neural network
models on embedded (micro-controller-driven) systems. TinyML will pave the way
for novel edge-level services and applications that survive on distributed edge
inferring and independent decision-making rather than server computation. In
this paper, we explore TinyML's methodology, how TinyML can benefit a few
specific industrial fields, its obstacles, and its future scope.
