# Management of Machine Learning Lifecycle Artifacts: A Survey

arXiv: https://arxiv.org/abs/2210.11831 · HTML: https://mlsanity.com/paper/2210.11831/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 148 references that are in the map.
- First posted on arXiv 21 Oct 2022.

## Builds on (most cited first, 21 of 21)

- [Horovod: fast and easy distributed deep learning in TensorFlow](https://mlsanity.com/paper/1802.05799/) (2018)
- [Clipper: A Low-Latency Online Prediction Serving System](https://mlsanity.com/paper/1612.03079/) (2016)
- [Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning](https://mlsanity.com/paper/1811.12808/) (2018)
- [Requirements Engineering for Machine Learning: Perspectives from Data Scientists](https://mlsanity.com/paper/1908.04674/) (2019)
- [Software Engineering Challenges of Deep Learning](https://mlsanity.com/paper/1810.12034/) (2018)
- [Rafiki: Machine Learning as an Analytics Service System](https://mlsanity.com/paper/1804.06087/) (2018)
- [Requirements Engineering Challenges in Building AI-Based Complex Systems](https://mlsanity.com/paper/1908.11791/) (2019)
- [Overton: A Data System for Monitoring and Improving Machine-Learned Products](https://mlsanity.com/paper/1909.05372/) (2019)
- [SystemDS: A Declarative Machine Learning System for the End-to-End Data Science Lifecycle](https://mlsanity.com/paper/1909.02976/) (2019)
- [MLModelCI: An Automatic Cloud Platform for Efficient MLaaS](https://mlsanity.com/paper/2006.05096/) (2020)
- [MLCask: Efficient Management of Component Evolution in Collaborative Data Analytics Pipelines](https://mlsanity.com/paper/2010.10246/) (2020)
- [ModelHub: Towards Unified Data and Lifecycle Management for Deep Learning](https://mlsanity.com/paper/1611.06224/) (2016)
- [Provenance Data in the Machine Learning Lifecycle in Computational Science and Engineering](https://mlsanity.com/paper/1910.04223/) (2019)
- [Workflow Provenance in the Lifecycle of Scientific Machine Learning](https://mlsanity.com/paper/2010.00330/) (2020)
- [ModelCI-e: Enabling Continual Learning in Deep Learning Serving Systems](https://mlsanity.com/paper/2106.03122/) (2021)
- [The Missing Piece in Complex Analytics: Low Latency, Scalable Model Management and Serving with Velox](https://mlsanity.com/paper/1409.3809/) (2014)
- [Asset Management in Machine Learning: A Survey](https://mlsanity.com/paper/2102.06919/) (2021)
- [sql4ml A declarative end-to-end workflow for machine learning](https://mlsanity.com/paper/1907.12415/) (2019)
- [ModelHub.AI: Dissemination Platform for Deep Learning Models](https://mlsanity.com/paper/1911.13218/) (2019)
- [Shuffler: A Large Scale Data Management Tool for ML in Computer Vision](https://mlsanity.com/paper/2104.05125/) (2021)
- [Apache Submarine: A Unified Machine Learning Platform Made Simple](https://mlsanity.com/paper/2108.09615/) (2021)

## Built on by (most cited first, 13 of 13)

- [Reproducibility in Machine Learning-based Research: Overview, Barriers and Drivers](https://mlsanity.com/paper/2406.14325/) (2024)
- [Don't Push the Button! Exploring Data Leakage Risks in Machine Learning and Transfer Learning](https://mlsanity.com/paper/2401.13796/) (2024)
- [An Empirical Study of Challenges in Machine Learning Asset Management](https://mlsanity.com/paper/2402.15990/) (2024)
- [SQL4NN: Validation and expressive querying of models as data](https://mlsanity.com/paper/2502.14745/) (2025)
- [Information Science Principles of Machine Learning: A Causal Chain Meta-Framework Based on Formalized Information Mapping](https://mlsanity.com/paper/2505.13182/) (2025)
- [Verifying Machine Learning Interpretability Requirements through Provenance](https://mlsanity.com/paper/2604.21599/) (2026)
- [VeML: An End-to-End Machine Learning Lifecycle for Large-scale and High-dimensional Data](https://mlsanity.com/paper/2304.13037/) (2023)
- [Model Callers for Transforming Predictive and Generative AI Applications](https://mlsanity.com/paper/2406.15377/) (2024)
- [What Makes Code Generation Ethically Sourced?](https://mlsanity.com/paper/2507.19743/) (2025)
- [Embedding Provenance in Computer Vision Datasets with JSON-LD](https://mlsanity.com/paper/2603.27348/) (2026)
- [Gypscie: A Cross-Platform AI Artifact Management System](https://mlsanity.com/paper/2604.10311/) (2026)
- [Workflow Cards: Structured Summaries of Workflow Executions Using Provenance Data](https://mlsanity.com/paper/2608.11022/) (2026)
- [Traceprop: Training Data Attribution in a Single Pass](https://mlsanity.com/paper/2609.32380/) (2026)

## Abstract (arXiv)

The explorative and iterative nature of developing and operating machine learning (ML) applications leads to a variety of artifacts, such as datasets, features, models, hyperparameters, metrics, software, configurations, and logs. In order to enable comparability, reproducibility, and traceability of these artifacts across the ML lifecycle steps and iterations, systems and tools have been developed to support their collection, storage, and management. It is often not obvious what precise functional scope such systems offer so that the comparison and the estimation of synergy effects between candidates are quite challenging. In this paper, we aim to give an overview of systems and platforms which support the management of ML lifecycle artifacts. Based on a systematic literature review, we derive assessment criteria and apply them to a representative selection of more than 60 systems and platforms.