@inproceedings{tran-etal-2026-mlops,
title = "{MLO}ps-Bench: Benchmarking {AI} Agents on Production {ML} Engineering in Real Repositories",
author = "Tran, Quoc Co and
Patel, Hitesh Laxmichand and
Mahajan, Diwakar and
Bakliwal, Kshitij and
Sil, Avi and
Kirchhoff, Katrin",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1402/",
pages = "29888--29903",
ISBN = "979-8-89176-462-0",
abstract = "AI agents are increasingly expected to modify and maintain machine-learning systems in production, yet existing benchmarks primarily evaluate either competition-style ML experimentation or generic repository issue resolution. We introduce MLOps-Bench, a repository-grounded benchmark for production ML engineering. Unlike prior ML-agent benchmarks, MLOps-Bench evaluates executable lifecycle changes inside real ML repositories, spanning data pipelines, feature engineering, model training, model serving, monitoring, CI/CD and governance, and end-to-end integration. Each task is grounded in repository evidence and includes a natural-language requirement, oracle patch, plausible buggy baseline, fail-to-pass tests, pass-to-pass tests, and provenance metadata. The benchmark contains 212 validated tasks from 31 public ML repositories, with 636 fail-to-pass tests and 856 pass-to-pass tests. We evaluate five agents and find that the best system solves 27.3{\%} of tasks while passing 67.1{\%} of tests, showing that current agents often make partial progress but still fail production-relevant behavioral requirements."
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%0 Conference Proceedings
%T MLOps-Bench: Benchmarking AI Agents on Production ML Engineering in Real Repositories
%A Tran, Quoc Co
%A Patel, Hitesh Laxmichand
%A Mahajan, Diwakar
%A Bakliwal, Kshitij
%A Sil, Avi
%A Kirchhoff, Katrin
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F tran-etal-2026-mlops
%X AI agents are increasingly expected to modify and maintain machine-learning systems in production, yet existing benchmarks primarily evaluate either competition-style ML experimentation or generic repository issue resolution. We introduce MLOps-Bench, a repository-grounded benchmark for production ML engineering. Unlike prior ML-agent benchmarks, MLOps-Bench evaluates executable lifecycle changes inside real ML repositories, spanning data pipelines, feature engineering, model training, model serving, monitoring, CI/CD and governance, and end-to-end integration. Each task is grounded in repository evidence and includes a natural-language requirement, oracle patch, plausible buggy baseline, fail-to-pass tests, pass-to-pass tests, and provenance metadata. The benchmark contains 212 validated tasks from 31 public ML repositories, with 636 fail-to-pass tests and 856 pass-to-pass tests. We evaluate five agents and find that the best system solves 27.3% of tasks while passing 67.1% of tests, showing that current agents often make partial progress but still fail production-relevant behavioral requirements.
%U https://aclanthology.org/2026.findings-emnlp.1402/
%P 29888-29903
Markdown (Informal)
[MLOps-Bench: Benchmarking AI Agents on Production ML Engineering in Real Repositories](https://aclanthology.org/2026.findings-emnlp.1402/) (Tran et al., Findings 2026)
ACL