Machine Inference

Components for machine learning inference:

• a data source
• a machine learning system to process the data,
• a data destination.

A data source may be multiple sources which are accumulating information from several places. In such s case when information is captured from an array of IoT inputs. A machine learning model employs a series of mathematical algorithms to sort and analyze live data as it comes in. Those algorithms can make deductions based on features in data—qualities that differentiate one item from another. The model then “scores” these data points and gives an output based on how it was trained to interpret the data. Those output scores can help determine the next course of action.

Currently, on Bittensor the validator is clocking 260 Billion parameters evaluations per second.

This is good for those who understand tech and languages but for those who are starting out it might be a bit confusing since it doesn't have directly any concern with blockchain. In simplest words, machine inference is when machine learning models are given data points meaning a set of data to calculate a numerical output which will then be translated to text, visual or audio.