With the increasing number of created and deployed prediction models and the complexity of machine learning workflows we require so called model management systems to support data scientists in their tasks. In this work we describe our technological concept for such a model management system. This concept includes versioned storage of data, support for different machine learning algorithms, fine tuning of models, subsequent deployment of models and monitoring of model performance after deployment. We describe this concept with a close focus on model lifecycle requirements stemming from our industry application cases, but generalize key features that are relevant for all applications of machine learning.
CITATION STYLE
Bachinger, F., & Kronberger, G. (2020). Concept for a Technical Infrastructure for Management of Predictive Models in Industrial Applications. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12013 LNCS, pp. 263–270). Springer. https://doi.org/10.1007/978-3-030-45093-9_32
Mendeley helps you to discover research relevant for your work.