Learning how Gaussian distributions and the properties they have can help us perform anomaly detection on monitored data. Monitoring solutions have been around since the first computers came into being in the middle of the last century and have been trying to answer one simple question ever since: “What’s happening inside my computer?”
Monitoring solutions have been around since the first computers came into being in the middle of the last century and have been trying to answer one simple question ever since: “What’s happening inside my computer?”. In the same way a teacher might ask a student to explain his thought process, we would like to gain knowledge about the internal workings of our program. If we figure out what went wrong during its runtime, we can gain better insights that can help us correct its behaviour.
The simplest way to answer that question might be writing data to any standard output, such as your computer screen or to a log file. Periodic logging at predefined intervals or when reaching a certain point at the execution of our program allows us to compare its state with the results we would have expected. If the output does not match the one we’d have expected, we can modify our code to correct the situation.
Over the years, computers have evolved in many different paths. The bulky computers you might have seen in an old movie have been replaced by laptops, mobile phones, IoT devices, network systems, industrial computers, you name it; These systems, which I will call “devices” from now on, usually have many applications in which we cannot monitor the internals of the given device. Since we still want to monitor their behaviour to verify they are running as expected, we need to find a new approach that can give us reliable results based on the output alone.
The initial approach was to write custom monitoring solutions for specific devices. For example, if we have a component that sends us the power consumption of an office building, we can write a program with a given set of instructions to monitor the output values of that device. While this was considered a good approach when such solutions were first made available, they have two major drawbacks. The first one is that we must know the allowed operating parameters of our program at the time of writing it, which in many cases we do not know. The second drawback is that even if we do know these parameters, we need to write a tailor-made algorithm with a fixed set of instructions for checking the device’s output is within the allowed operating parameters.
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It generally covers statistics, mathematics, physics, economics, business, and management. Here, we’ll go to different reasons for those undergraduates to learn statistical programming.
Statistics for Data Science and Machine Learning Engineer. I’ll try to teach you just enough to be dangerous, and pique your interest just enough that you’ll go off and learn more.
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