Machine learning, also abbreviated to ML, is a variant of artificial intelligence, in which statistical methods can be used to improve the performance of an algorithm in identifying a pattern in the data.
Machine Learning is therefore an alternative to traditional programming: instead of giving explicit and sequential commands or instructions, the machine is designed to be able to analyze data, learn and act in an autonomous way.
This behavior is closely related to the recognition of specific patterns and the construction of algorithms, thanks to a model based on samples: this allows to make predictions about the functioning and possible corrective actions, and it’s known as predictive analysis.
Machine Learning in companies
Artificial Intelligence (AI) and Machine Learning are topics that in recent years, and even more recently, have been in the spotlight and many are wondering how to apply them within business processes, without forgetting their limits.
One of the most common challenges to almost all organizations has always been improving the decision-making process through data analysis, to track future events and to predict scenarios and consequences.
Traditional analysis systems use descriptive models and expert know-how to establish correlations between variables and to formulate a prediction. But what happens when data changes dynamically?
Machine Learning comes into play just when designing and programming explicit algorithms through a finite number of instructions and unambiguous rules becomes an impossible undertaking: yes, it needs large amounts of data to learn, but it automatically selects the variables and their interactions starting from the goal to be achieved, trying to learn which are the most important factors that will allow the achievement of that goal.
From this point of view, Machine Learning therefore becomes the key element in the analysis of the data itself, because it learns from the data and chooses with the ultimate aim of achieving the goal. Tasks that would be impossible to program in the traditional way, with sequential commands or instructions.
The predictive analysis of electrical panels
Sensis by Fandis is the first IIoT device in thermal management which, in addition to measuring the climatic quantities in the cabinet and controlling the devices to maintain the optimal temperature level, is able to process information and recognize anomalous events, thanks to predictive analytics.
To find out more about Sensis and all our products for enclosures, visit our website fandis.com, browse our blog or send an email to firstname.lastname@example.org. Our staff will answer you as soon as possible.
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