The last decade of the Industry 4.0 revolution has shown the value and importance of machine learning (ML) across verticals and environments, with more impact on manufacturing than possibly any other application. Organizations implementing a more automated, reliable, and cost-effective Operational Technology (OT) strategy have led the way, recognizing the benefits of ML in predicting assembly line failures to avoid costly and unplanned downtime. Still, challenges remain for teams of all sizes to quickly, and with little effort, demonstrate the value of ML-based anomaly detection in order to persuade management and finance owners to allocate the budget required to implement these new technologies. Without access to data scientists for model training, or ML specialists to deploy solutions at the local level, adoption has seemed out of reach for teams on the factory floor.
Now, teams that collect sensor data signals from machines in the factory can unlock the power of

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