Advances in machine learning (ML) techniques allow practitioners to generate substantial predictive value from historical data. Modern sensors generate vast amounts of data which inform prognostic health management (PHM) systems. As ML techniques continue to grow in importance for PHM, the system that manages and deploys ML models becomes critical for successful production software. Machine Learning Operations (MLOps) is centered around implementing continuous integration and deployment (CI/CD) practices in the context of ML applications. We will present MLOps designs for deploying machine learning based PHM software and discuss ML pipelines that automate data ingestion, model training, testing, deployment, and monitoring. The principles we will examine ensure model quality, performance, and software stability. We will call attention to important design considerations and demonstrate solutions for the full model lifecycle when building MLOps pipelines for PHM systems.
Machine Learning, Artificial Intelligence, PHM, MLOps, Big Data
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