Alerting and Monitoring Machine Learning Model

Our project offers Ideabytes’ clients a system that can predict future values and detect performance anomalies with a decent level of accuracy using a machine learning model trained on the client’s specific IoT device. Using this machine learning model one can find previously unseen patterns and trends within datasets and make predictions of potential issues in the future, such as efficiency degradation or potentially broken equipment. The end goal is to give users a warning beforehand to avoid potentially spoiling merchandise or causing interruptions in production efficiency.

Key results of the model include:
– Learns from previously recorded data without any human guidance
– Predicts future data points with an accuracy of above 70%
– Detects anomalous data points that do not fit within the anticipated pattern
– Alerts the user if data has gone outside acceptable thresholds

A mock environment was developed to imitate the production environment of Ideabytes for the model to execute within. This way, once completed, it’d be easily integrated into Ideabytes’ existing ecosystem and immediately available for clients without any headache.