Machine Learning is valuable when you don’t know the important input variables to make that decision and therefore you give the Machine Learning algorithm the goal and then it learns the from the data, that which factors are important in achieving the goal.
A great example is Google’s application of machine learning to its data centers last year. Data centers need to remain cool, so they require vast amounts of energy for their cooling systems to function properly. This represents a significant cost to Google, so the goal was to increase efficiency with machine learning.
With 120 variables affecting the cooling system (i.e. fans, pumps, speeds, windows, etc.), building a model with classic approaches would be a huge undertaking. Instead, Google applied machine learning and cut its overall energy consumption by 15%.
That represents hundreds of millions of dollars in savings for Google in the coming years.
And therefore, We at Primesophic Technologies have started a revolution of helping small and large organizations with expert digital consultations by which organizations can make easy decisions in upgrading and scaling their tech stack for past and current projects thereby saving vast amount of time, money and energy.
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