Concept for a Technical Infrastructure for Management of Predictive Models in Industrial Applications
This addresses the need for efficient model lifecycle management for data scientists in industrial applications, but it is incremental as it builds on existing model management ideas with a specific focus.
The paper tackles the problem of managing numerous and complex machine learning workflows in industrial settings by proposing a technical infrastructure concept for model management systems, which includes versioned data storage, algorithm support, fine-tuning, deployment, and performance monitoring.
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.