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

arXiv: https://arxiv.org/abs/2107.13821 · HTML: https://mlsanity.com/paper/2107.13821/

## Key facts

- Cited by 1 papers in the map (data through 7 Oct 2026).
- Builds on 2 of its 11 references that are in the map.
- First posted on arXiv 29 Jul 2021.

## Builds on (most cited first, 2 of 2)

- [Clipper: A Low-Latency Online Prediction Serving System](https://mlsanity.com/paper/1612.03079/) (2016)
- [Machine Learning at Microsoft with ML .NET](https://mlsanity.com/paper/1905.05715/) (2019)

## Built on by (most cited first, 1 of 1)

- [The Pipeline for the Continuous Development of Artificial Intelligence Models -- Current State of Research and Practice](https://mlsanity.com/paper/2301.09001/) (2023)

## Abstract (arXiv)

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.