The DOI matches its Crossref record; 9 of the 10 references it cites were found in a registry or an index.
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.
Overview
Integrity signals
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Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
Citations
Cites
XGBoost: A Scalable Tree Boosting System
Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569
TensorFlow: A system for large-scale machine learningPreprint
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al. (2016) · arXiv (Cornell University) · doi:10.5555/3026877.3026899 · Cited by 16,548
Data Structures for Statistical Computing in Python
McKinney, W. (2010) · Proceedings of the Python in Science Conferences · doi:10.25080/majora-92bf1922-00a · Cited by 12,930
Apache Spark: A unified engine for big data processing
Zaharia, M., Xin, R. S., Wendell, P., Das, T., Armbrust, M., Dave, A., Meng, X., Rosen, J., Venkataraman, S., Franklin, M. J., et al. (2016) · Communications of the ACM · doi:10.1145/2934664 · Cited by 2,757
Self-citationClipper: A Low-Latency Online Prediction Serving System
Crankshaw, D., Wang, X., Zhou, G., Franklin, M. J., Gonzalez, J. E., & Stoica, I. (2016) · arXiv · doi:10.48550/arxiv.1612.03079 · Cited by 415
TFX: A TensorFlow-Based Production-Scale Machine Learning Platform
Baylor, D., Breck, E., Cheng, H.-T., Fiedel, N., Foo, C. Y., Haque, Z., Haykal, S., Ispir, M., Jain, V., Koc, L., et al. (2017) · Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/3097983.3098021 · Cited by 350
Self-citationVartak, M., Subramanyam, H., Lee, W.-E., Viswanathan, S., Husnoo, S., Madden, S. R., & Zaharia, M. (2016) · Workshop on Human-In-the-Loop Data Analytics (HILDA) · doi:10.1145/2939502.2939516 · Cited by 157
Self-citationProvenance Analysis for Missing Answers and Integrity Repairs.
Zaharia, M., Chen, A., Davidson, A., Ghodsi, A., Hong, S. A., Konwinski, A., Murching, S., Nykodym, T., Ogilvie, P., Parkhe, M., et al. (2018) · IEEE Data(base) Engineering Bulletin · Cited by 112
Self-citationThe Sacred Infrastructure for Computational Research
Greff, K., Klein, A., Chovanec, M., Hutter, F., & Schmidhuber, J. (2017) · Proceedings of the Python in Science Conferences · doi:10.25080/shinma-7f4c6e7-008 · Cited by 63
Flagged first, then by citations
1 of the 10 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- M. Zaharia A. Chen A. Davidson A. Ghodsi S.A. Hong A. Konwinski S. Murching T. Nykodym P. Ogilvie M. Parkhe F. Xie and C. Zumar. 2018. Accelerating the Machine Learning Lifecycle with MLflow. IEEE Data Engineering Bulletin 41(4) (2018).
Cited by
Sorted by citations
Items 101–112 of 112
Segmentation of Pre and Post-treatment Brain Metastases Using nnU-Nets
Bancerek, M., Rudzki, P., & Nalepa, J. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-16365-3_15
Soares, F., Barbosa, T., Barbosa‐Breda, J., Pinto, L. A., & Marques-Neves, C. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-19105-2_23
Okafor, O., Adama, D. A., Dangana, M., & Vinkemeier, D. (2026) · IEEE Sensors Journal · doi:10.1109/jsen.2026.3706851
Panagoulias, D. P., Damian, A. I., Stamate, C., Toderian, V., Butusina, P., Franceschi, A. D., Bleotiu, C., Sakkopoulos, E., & Tsichrintzi, E.-A. (2026) · Electronics · doi:10.3390/electronics15143046 · Cited by 1
Iwański, M., Regulski, P. A., & Wendykier, P. (2026) · Diagnostics · doi:10.3390/diagnostics16142214
DeepFix: Debugging and Fixing Machine Learning Workflow using Agentic AI
Seydou, F. M., & Sharma, A. (2026) · IEEE International Conference on Software Testing, Verification and Validation (ICST) · doi:10.1109/icst69053.2026.00047
From Prediction to Decision: A Multihorizon Completion Optimization Framework for the Bakken Field
Ren, Z., Male, F., & H., L. F. A. (2026) · SPE Journal · doi:10.2118/234694-pa
Consist: Provenance-Aware Caching and Queryable Provenance for Scientific Workflows
Needell, Z. A. (2026) · The Journal of Open Source Software · doi:10.21105/joss.11131
Demonstrating EarthLake: A Model Lake System for Earth Observation Foundation Model Management
Chen, B., Gavriilidis, H., Gaedicke, L., Bök, T. E., Boehm, M., Abedjan, Z., Demir, B., & Markl, V. (2026) · Proceedings of the VLDB Endowment · doi:10.14778/3827998.3828078
Damian, A. I., Panagoulias, D. P., Stamate, C., Tsichrintzi, E.-A., Toderian, V., & Sakkopoulos, E. (2026) · International Conference on Information, Intelligence, Systems & Applications (IISA) · doi:10.1109/iisa71656.2026.11705526
Automated Payment Verification of Legal Documents
Carpi, M. D. M., Lima, J. P., Bitelli, B., Ferrari, E., & Finger, M. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-39898-7_10
Noordijk, B., van Loo, T., van Hoek, M., Reinders, M. J. T., van Dijk, A. D. J., García‐Gómez, M. L., ten Tusscher, K., & de Ridder, D. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.10.20.758482
Metadata
- 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.
What counts as a claim ›How we measure confidence ›How we check this ›
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