Autonomously Adaptive Machine Learning Systems: Experimentation-Driven Open-Source Pipeline.

Machine Learning Operations (MLOps), derived from DevOps, aims to unify the development, deployment, and maintenance of machine learning (ML) models. Continuous training (CT) automatically retrains ML models, and continuous deployment (CD) automatically deploys the retrained models to production. CT and CD are essential for maintaining ML model performance in dynamic production environments and, therefore, need to be considered when practicing MLOps. We present our CTCD-e MLOps pipeline, implemented mostly using existing open-source software, being able to autonomously adapt an ML system to changing production environments by enabling flexible model CT and CD. The pipeline can automatically trigger a model retraining round when the model performance degrades. Then it automatically conducts an A/B test for the retrained model and its predecessor in production to start serving the better one. The pipeline was evaluated by two experiments. In the pipeline, users can flexibly configure the model retraining, as well as the redeployment and production A/B test of the retrained models based on various requirements.