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

TL;DR

This paper proposes a comprehensive model management system architecture that supports versioning, deployment, and monitoring of machine learning models tailored for industrial applications.

Contribution

It introduces a technological concept for a model management system addressing lifecycle requirements specific to industrial machine learning workflows.

Findings

Supports versioned data storage and multiple algorithms

Enables fine-tuning and deployment of models

Provides monitoring of model performance post-deployment

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.