A software system architecture for managing machine learning model lifecycle including training, deployment, drift detection, and operational monitoring.

Patentradars sammanfattning

The invention describes a comprehensive ML operations (MLOps) framework comprising two integrated pipelines: a training pipeline that handles data ingestion, preprocessing, model training, evaluation, and registration, and an inference pipeline that manages deployment, monitoring, and drift detection. The system enables continuous model performance tracking and automated detection of data/model degradation in production environments, supporting operational oversight of deployed models.

AI-genererad och redaktionellt processad.