Automated Model Re-Training Framework Based on Expert Feedback

INNIO Digital has deployed more than 70 models for anomaly detection and preventive maintenance on their cloud-based platform myPlant, using Machine Learning methods on time series data gathered from millions of sensors in the Industrial Internet of Things. The goal of this thesis is to 

• Conceptualize a framework for automated computations of accuracy scores of issue detection algorithms based on various inputs (e.g., detected issues with feedback labels from experts)
• Design a system for evaluating the accuracy scores and triggering re-training workflows when necessary, with subsequent validations of the impact of the re-training (improvement of model accuracy)
• Demonstrate feasibility by implementing an end-to-end example using an existing model with a given
  re-training workflow specification.