# Model Lake: a New Alternative for Machine Learning Models Management and Governance

arXiv: https://arxiv.org/abs/2503.22754 · HTML: https://mlsanity.com/paper/2503.22754/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 17 references that are in the map.
- First posted on arXiv 27 Mar 2025.

## Builds on (most cited first, 3 of 3)

- [Machine Learning Operations (MLOps): Overview, Definition, and Architecture](https://mlsanity.com/paper/2205.02302/) (2022)
- [An Empirical Study of Pre-Trained Model Reuse in the Hugging Face Deep Learning Model Registry](https://mlsanity.com/paper/2303.02552/) (2023)
- [What's documented in AI? Systematic Analysis of 32K AI Model Cards](https://mlsanity.com/paper/2402.05160/) (2024)

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

- [An experimental survey and Perspective View on Meta-Learning for Automated Algorithms Selection and Parametrization](https://mlsanity.com/paper/2504.06207/) (2025)

## Abstract (arXiv)

The rise of artificial intelligence and data science across industries underscores the pressing need for effective management and governance of machine learning (ML) models. Traditional approaches to ML models management often involve disparate storage systems and lack standardized methodologies for versioning, audit, and re-use. Inspired by data lake concepts, this paper develops the concept of ML Model Lake as a centralized management framework for datasets, codes, and models within organizations environments. We provide an in-depth exploration of the Model Lake concept, delineating its architectural foundations, key components, operational benefits, and practical challenges. We discuss the transformative potential of adopting a Model Lake approach, such as enhanced model lifecycle management, discovery, audit, and reusability. Furthermore, we illustrate a real-world application of Model Lake and its transformative impact on data, code and model management practices.