TinyML, or Tiny Machine Learning, is a subset of machine learning that allows models to run on smaller, less powerful devices. It uses hardware, software, and algorithms to analyze sensor data on these devices.
TinyML is important because it allows advanced machine learning functions to be performed on smaller devices. It has several advantages, including:
Low power consumption: TinyML algorithms can reduce the computational load and minimize data transfer, enabling energy-efficient operation.
Low latency: Data doesn't have to be sent to a server to run inference.
Low bandwidth: Less internet bandwidth is used because data doesn't have to be sent to the server constantly.
TinyML can be challenging due to hardware constraints, such as limited memory resources and compiler and inference engine support.
In this project i am using raspberry pi 2 having 1gb of ram and tensorflowlite for low end embbded systems and IOT
prediction of real time data from BMP280 sensor on raspberry pi 2 using RabbitMQ and Linear Neural Network
RabbitMQ is a message-queueing software also known as a message broker or queue manager. Simply said; it is software where queues are defined, to which applications connect in order to transfer a message or messages.
