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Even basic predictivemodeling can be done with lightweight machine learning in Python or R. Imagine such a system processing unstructured text data like historical maintenance logs, technician notes, defect reports and warranty claims, and correlating it with structured sensor data such as IoT readings and machine telemetry.
The counterexample to the supervised learning explanation of precursor analytics is a “black swan” event – a rare high-impact event that is difficult to predict under normal circumstances – such as the global pandemic, which led to the failure of many predictivemodels in business. Pay attention!
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