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Production Machine Learning Pipelines: Empirical Analysis and Optimization Opportunities

Summary: Analyzes 3,000 production ML pipelines at Google via provenance graphs and 450k trainings to characterize lifespan, topology, and complexity. Introduces model graphlets, a data model for repeated components, and shows optimization opportunities—pruning wasted computation can cut costs by ~50% without delaying deployment. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
h2094ded95d203b3f
Venue
SIGMOD
Year
2021
Pagerank
8.8854572e-05
Overall Rank
2,189 | 85.29%
DOI
10.1145/3448016.3457566
PDF
Download (CC BY 4.0)

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{xin_sigmod21,
        title = {{Production Machine Learning Pipelines: Empirical Analysis and Optimization Opportunities}},
        author = {Xin, Doris and Miao, Hui and Parameswaran, Aditya and Polyzotis, Neoklis},
        series = {{SIGMOD} '21},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3448016.3457566},
        url = {https://dl.acm.org/doi/10.1145/3448016.3457566},
        year = {2021}
}

Incoming Citations (Sorted by Pagerank)

Showing 16 of 16 citing papers.

Rank Citing Paper Year Venue Pagerank
4,926 TPCx-AI - An Industry Standard Benchmark for Artificial Intelligence and Machine Learning Systems 2023 VLDB 6.3481677e-05
6,666 UPLIFT: Parallelization Strategies for Feature Transformations in Machine Learning Workloads 2022 VLDB 5.7144587e-05
6,924 CtxPipe: Context-aware Data Preparation Pipeline Construction for Machine Learning 2024 SIGMOD 5.6405901e-05
7,544 Automating and Optimizing Data-Centric What-If Analyses on Native Machine Learning Pipelines 2023 SIGMOD 5.496984e-05
8,389 AWARE: Workload-aware, Redundancy-exploiting Linear Algebra 2023 SIGMOD 5.3396465e-05
8,978 Pipemizer: An Optimizer for Analytics Data Pipelines 2022 VLDB 5.2446911e-05
9,079 Scheduling Data Processing Pipelines for Incremental Training on MLP-based Recommendation Models 2025 SIGMOD 5.2258409e-05
9,126 Modyn: Data-Centric Machine Learning Pipeline Orchestration 2025 SIGMOD 5.2238659e-05
10,864 PipeLens: Identifying Interventions for Resolving Malfunctioning Data Science Pipelines 2026 VLDB 4.9769913e-05
10,907 stratum: A System Infrastructure for Massive Agent-Centric ML Workloads 2026 VLDB 4.9769913e-05
10,941 IMLane: Composable Framework for Efficient AI Function Execution in Database Engine 2026 VLDB 4.9769913e-05
10,970 SemPiper: Interactive Code Synthesis for Semantic Operators in Machine Learning Pipelines 2026 VLDB 4.9769913e-05
11,416 APEX-DAG: Library and Language independent Pipeline EXtraction 2025 VLDB 4.9769913e-05
11,595 Efficiently Mitigating the Impact of Data Drift on Machine Learning Pipelines 2024 VLDB 4.9769913e-05
11,737 Demystifying the QoS and QoE of Edge-hosted Video Streaming Applications in the Wild with SNESet 2023 SIGMOD 4.9769913e-05
11,760 Enabling Secure and Efficient Data Analytics Pipeline Evolution with Trusted Execution Environment 2023 VLDB 4.9769913e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 14 of 14 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
105 The MADlib Analytics Library or MAD Skills, the SQL 2012 VLDB 0.00033633007
509 Goods: Organizing Google's Datasets 2016 SIGMOD 0.00017063491
1,153 Data Management Challenges in Production Machine Learning 2017 SIGMOD 0.00011793347
1,564 Titian: Data Provenance Support in Spark 2016 VLDB 0.00010219222
1,568 HELIX: Holistic Optimization for Accelerating Iterative Machine Learning 2019 VLDB 0.00010208225
1,767 Putting Lipstick on Pig: Enabling Database-style Workflow Provenance 2012 VLDB 9.6910812e-05
1,881 Ground: A Data Context Service 2017 CIDR 9.4372419e-05
1,928 Elastic Machine Learning Algorithms in Amazon SageMaker 2020 SIGMOD 9.3563363e-05
2,184 Extending Relational Query Processing with ML Inference 2020 CIDR 8.8953085e-05
2,447 noWorkflow: a Tool for Collecting, Analyzing, and Managing Provenance from Python Scripts 2017 VLDB 8.4530494e-05
4,161 The Relational Data Borg is Learning 2020 VLDB 6.7669004e-05
4,804 Improving Reproducibility of Data Science Pipelines through Transparent Provenance Capture 2020 VLDB 6.4074452e-05
5,749 An Optimal Labeling Scheme for Workflow Provenance Using Skeleton Labels 2010 SIGMOD 6.0051184e-05
6,223 Incremental View Maintenance For Collection Programming 2016 PODS 5.8443814e-05
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