figure - machine learning paradigm figure - reinforces the fact that in the machine learning paradigmthe machinein this context the computertries to use input data and expected outputs to try to learn inherent patterns in the data that would ultimately help in building model analogous to computer programwhich would help in making data-driven decisions in the future (predict or tell us the outputfor new input data points by using the learned knowledge from previous data points (its knowledge or experienceyou might start to see the benefit in this we would not need hand-coded rulescomplex flowchartscase and if-then conditionsand other criteria that are typically used to build any decision making system or decision support system the basic idea is to use machine learning to make insightful decisions this will be clearer once we discuss our real-world problem of managing infrastructure for dss company in the traditional programming approachwe talked about hiring new staffsetting up rule-based monitoring systemsand so on if we were to use machine learning paradigm shift herewe could go about solving the problem using the following steps leverage device data and logs and make sure we have enough historical data in some data store (databaselogsor flat filesdecide key data attributes that could be useful for building model this could be device usagelogsmemoryprocessorconnectionsline strengthlinksand so on observe and capture device attributes and their behavior over various time periods that would include normal device behavior and anomalous device behavior or outages these outcomes would be your outputs and device data would be your inputs feed these input and output pairs to any specific machine learning algorithm in your computer and build model that learns inherent device patterns and observes the corresponding output or outcome deploy this model such that for newer values of device attributes it can predict if specific device is behaving normally or it might cause potential outage thus once you are able to build machine learning modelyou can easily deploy it and build an intelligent system around it such that you can not only monitor devices reactively but you would be able to proactively identify potential problems and even fix them before any issues crop up imagine building self-heal or auto-heal systems coupled with round the clock device monitoring the possibilities are indeed endless and you will not have to keep on hiring new staff every time you expand your office or buy new infrastructure of coursethe workflow discussed earlier with the series of steps needed for building machine learning model is much more complex than how it has been portrayedbut again this is just to emphasize and make you think more conceptually rather than technically of how the paradigm has shifted in case