Developments in MLflow: A System to Accelerate the Machine Learning Lifecycle
Abstract
MLflow is a popular open source platform for managing ML development, including experiment tracking, reproducibility, and deployment. In this paper, we discuss user feedback collected since MLflow was launched in 2018, as well as three major features we have introduced in response to this feedback: a Model Registry for collaborative model management and review, tools for simplifying ML code instrumentation, and experiment analytics functions for extracting insights from millions of ML experiments.
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The model could also be packaged in a format specific to an end-to-end framework such as MLflow.
“The model could also be packaged in a format specific to an end-to-end framework such as MLflowfoot_4 [10].”
From Operationalizing machine learning models: a systematic literature review (2022) · cites this paper as [10]
Edge deployments are often reported as being more challenging than cloud deployments because of hardware resource heterogeneity, the typically distributed nature of edge devices, and possibly varying network connection quality.
“Edge deployments are often reported as being more challenging than cloud deployments because of hardware resource heterogeneity, the typically distributed nature of edge devices, and possibly varying network connection quality [7,10,20,22,39,53].”
From Operationalizing machine learning models: a systematic literature review (2022) · cites this paper as [10]
Polyaxon, MLflow, TFX, ZenML, Flyte, Seldon Core, BentoML, and Amazon SageMaker are among the more fully integrated platforms for operationalizing ML.
“Polyaxon 9 , MLflow 10 , TFX 11 , ZenML 12 , Flyte 13 , Seldon Core 14 and BentoML 15 are some of the more fully-integrated platforms that exist [10,45], along with the Amazon-exclusive Sage-Maker 16 [9,45].”
From Operationalizing machine learning models: a systematic literature review (2022) · cites this paper as [10]
Monitoring ML models is challenging but is required in order to detect performance degradations and build confidence in the models.
“Monitoring ML models is challenging [6,28,41], but is required in order to detect performance degradations and build confidence in the models [7,10].”
From Operationalizing machine learning models: a systematic literature review (2022) · cites this paper as [10]
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de Conto, E., Genest, B., & Easwaran, A. (2024) · ACM International Conference on AI-Powered Software (AIware) · doi:10.1145/3664646.3664759
Peters, H., & Parrott, M. (2024) · Proceedings of the Python in Science Conferences · doi:10.25080/mdce8355
Evaluating Custom-Precision Operator Support in MLIR for ARM CPUs
Mücke, M., & Gratl, C. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-74643-7_12
Upreti, D., Maheshwari, A., Tabb, T., Polykretis, I., Gallo, E. M., Stewart, K. M., LaToza, T. D., & Danielescu, A. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713249
From Acquiring to Suggesting DL Design Choices with Agility: A System Design
dos Reis, G. R., Cornax, M. C., Mos, A., & Labbé, C. (2025) · Lecture notes in business information processing · doi:10.1007/978-3-031-92471-2_15
Hierarchical classification of research fields in the MAG science network using deep learning
Rao, S. X., Egger, P. H., & Zhang, C. (2025) · Quantitative Science Studies · doi:10.1162/qss.a.2 · Cited by 7 · see citing works
Hernández-Bonilla, J. E., Wendt, T., Reuschel, T., Yang, C. F., & Schuster, C. (2025) · IEEE Workshop on Signal and Power Integrity · doi:10.1109/spi64682.2025.11014374
Architectural Proposal for Reproducible, Standardized Deep Learning Research
Lübbering, M., Shah, V., Chatterjee, M., Priya, P., Soliman, O. I. I., & Sifa, R. (2025) · IEEE International Conference on Software Architecture Companion (ICSA-C) · doi:10.1109/icsa-c65153.2025.00021
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The Cost of Teaching Operational ML
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- Authors
- Chen, A., Chow, A., Davidson, A., DCunha, A., Ghodsi, A., Hong, S. A., Konwinski, A., Mewald, C., Murching, S., Nykodym, T., Ogilvie, P., Parkhe, M., Singh, A., Xie, F., Zaharia, M., Zang, R., Zheng, J., & Zumar, C.
- Affiliations
- Databricks (United States) (US)
- Databricks, Inc.
- Venue
- Proceedings of the Fourth International Workshop on Data Management for End-to-End Machine Learning
- Publisher
- ACM
- Published
- 2020-06-14
- Pages
- 1-4
- OpenAlex
- W3036963153
- Cited by
- 112
- References
- 10
- Citations by year
- 2026: 15 · 2025: 32 · 2024: 18 · 2023: 17 · 2022: 17 · 2021: 11 · 2020: 2
- FWCI
- 25.12
- Type
- conference paper
- Language
- en
- Open access
- closed
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
- Keywords
- MLflow, machine learning platform, experiment tracking, reproducibility, model deployment, model registry, user feedback
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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