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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
8.8896655e-05
Overall Rank
2,187 | 85.30%
DOI
10.1145/3448016.3457566

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
4,925 TPCx-AI - An Industry Standard Benchmark for Artificial Intelligence and Machine Learning Systems 2023 VLDB 6.3511742e-05
6,662 UPLIFT: Parallelization Strategies for Feature Transformations in Machine Learning Workloads 2022 VLDB 5.7171651e-05
6,921 CtxPipe: Context-aware Data Preparation Pipeline Construction for Machine Learning 2024 SIGMOD 5.6432616e-05
7,538 Automating and Optimizing Data-Centric What-If Analyses on Native Machine Learning Pipelines 2023 SIGMOD 5.4995874e-05
8,384 AWARE: Workload-aware, Redundancy-exploiting Linear Algebra 2023 SIGMOD 5.3421754e-05
8,968 Pipemizer: An Optimizer for Analytics Data Pipelines 2022 VLDB 5.2471751e-05
9,070 Scheduling Data Processing Pipelines for Incremental Training on MLP-based Recommendation Models 2025 SIGMOD 5.2283159e-05
9,117 Modyn: Data-Centric Machine Learning Pipeline Orchestration 2025 SIGMOD 5.2263399e-05
10,855 PipeLens: Identifying Interventions for Resolving Malfunctioning Data Science Pipelines 2026 VLDB 4.9793485e-05
10,898 stratum: A System Infrastructure for Massive Agent-Centric ML Workloads 2026 VLDB 4.9793485e-05
10,932 IMLane: Composable Framework for Efficient AI Function Execution in Database Engine 2026 VLDB 4.9793485e-05
10,961 SemPiper: Interactive Code Synthesis for Semantic Operators in Machine Learning Pipelines 2026 VLDB 4.9793485e-05
11,410 APEX-DAG: Library and Language independent Pipeline EXtraction 2025 VLDB 4.9793485e-05
11,589 Efficiently Mitigating the Impact of Data Drift on Machine Learning Pipelines 2024 VLDB 4.9793485e-05
11,731 Demystifying the QoS and QoE of Edge-hosted Video Streaming Applications in the Wild with SNESet 2023 SIGMOD 4.9793485e-05
11,754 Enabling Secure and Efficient Data Analytics Pipeline Evolution with Trusted Execution Environment 2023 VLDB 4.9793485e-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 0.00033638251
509 Goods: Organizing Google's Datasets 2016 SIGMOD 0.00017071087
1,153 Data Management Challenges in Production Machine Learning 2017 SIGMOD 0.00011798912
1,564 Titian: Data Provenance Support in Spark 2016 VLDB 0.00010222394
1,568 HELIX: Holistic Optimization for Accelerating Iterative Machine Learning 2019 VLDB 0.0001021302
1,765 Putting Lipstick on Pig: Enabling Database-style Workflow Provenance 2012 VLDB 9.6955585e-05
1,880 Ground: A Data Context Service 2017 CIDR 9.4415148e-05
1,927 Elastic Machine Learning Algorithms in Amazon SageMaker 2020 SIGMOD 9.3607648e-05
2,182 Extending Relational Query Processing with ML Inference 2020 CIDR 8.8982998e-05
2,445 noWorkflow: a Tool for Collecting, Analyzing, and Managing Provenance from Python Scripts 2017 VLDB 8.4570447e-05
4,161 The Relational Data Borg is Learning 2020 VLDB 6.7700593e-05
4,801 Improving Reproducibility of Data Science Pipelines through Transparent Provenance Capture 2020 VLDB 6.4104797e-05
5,748 An Optimal Labeling Scheme for Workflow Provenance Using Skeleton Labels 2010 SIGMOD 6.0079624e-05
6,219 Incremental View Maintenance For Collection Programming 2016 PODS 5.8471493e-05
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