Concept for a Technical Infrastructure for Management of Predictive Models in Industrial Applications

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationComputer Aided Systems Theory – EUROCAST 2019 - 17th International Conference, Revised Selected Papers
EditorsRoberto Moreno-Díaz, Alexis Quesada-Arencibia, Franz Pichler
PublisherSpringer
Pages263-270
Number of pages8
ISBN (Print)9783030450922
DOIs
Publication statusPublished - 2020
Event17th International Conference on Computer Aided Systems Theory, EUROCAST 2019 - Las Palmas de Gran Canaria, Spain
Duration: 17 Feb 201922 Feb 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12013 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th International Conference on Computer Aided Systems Theory, EUROCAST 2019
Country/TerritorySpain
CityLas Palmas de Gran Canaria
Period17.02.201922.02.2019

Keywords

  • Machine learning workflow
  • Model lifecycle
  • Model management
  • Software architecture concept

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