Authors:
Christian Weber
1
;
Pascal Hirmer
2
;
Peter Reimann
1
and
Holger Schwarz
2
Affiliations:
1
Graduate School advanced Manufacturing Engineering, University of Stuttgart, Nobelstraße 12, 70569 Stuttgart and Germany
;
2
Institute for Parallel and Distributed Systems, University of Stuttgart, 70569 Stuttgart and Germany
Keyword(s):
Model Management, Machine Learning, Analytics Process.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Data Engineering
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Health Information Systems
;
Industrial Applications of Artificial Intelligence
;
Information Systems Analysis and Specification
;
Knowledge Management
;
Ontologies and the Semantic Web
;
Sensor Networks
;
Signal Processing
;
Society, e-Business and e-Government
;
Soft Computing
;
Tools, Techniques and Methodologies for System Development
;
Web Information Systems and Technologies
Abstract:
The management of machine learning models is an extremely challenging task. Hundreds of prototypical models are being built and just a few are mature enough to be deployed into operational enterprise information systems. The lifecycle of a model includes an experimental phase in which a model is planned, built and tested. After that, the model enters the operational phase that includes deploying, using, and retiring it. The experimental phase is well known through established process models like CRISP-DM or KDD. However, these models do not detail on the interaction between the experimental and the operational phase of machine learning models. In this paper, we provide a new process model to show the interaction points of the experimental and operational phase of a machine learning model. For each step of our process, we discuss according functions which are relevant to managing machine learning models.