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This suggests that machine learning models can effectively accommodate approximate input features with minimal discernible impact on accuracy. In this paper, we introduce Biathlon, a novel ML serving system that leverages the inherent resilience of models and determines the optimal degree of approximation for each aggregation feature. This approach enables maximum speedup while ensuring a guaranteed bound on accuracy loss. We evaluate Biathlon on real pipelines from both industry applications and data science competitions, demonstrating its ability to meet real-time latency requirements by achieving 5.3\u00d7 to 16.6\u00d7 speedup with almost no accuracy loss.<\/jats:p>","DOI":"10.14778\/3675034.3675052","type":"journal-article","created":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T22:19:11Z","timestamp":1722982751000},"page":"2631-2640","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Biathlon: Harnessing Model Resilience for Accelerating ML Inference Pipelines"],"prefix":"10.14778","volume":"17","author":[{"given":"Chaokun","family":"Chang","sequence":"first","affiliation":[{"name":"The Chinese University of Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eric","family":"Lo","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunxiao","family":"Ye","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,8,6]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"4paradigm. 2023. 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In Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data (Snowbird, Utah, USA) (SIGMOD '14). Association for Computing Machinery, New York, NY, USA, 481--492. 10.1145\/2588555.2593667"},{"key":"e_1_2_1_4_1","volume-title":"BlinkDB: Queries with Bounded Errors and Bounded Response Times on Very Large Data. arXiv: Databases,arXiv: Databases (Mar","author":"Agarwal Sameer","year":"2012","unstructured":"Sameer Agarwal, Aurojit Panda, Barzan Mozafari, Samuel Madden, and Ion Stoica. 2012. BlinkDB: Queries with Bounded Errors and Bounded Response Times on Very Large Data. arXiv: Databases,arXiv: Databases (Mar 2012)."},{"key":"e_1_2_1_5_1","volume-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 10","author":"Ahmed Zeeshan","year":"2019","unstructured":"Zeeshan Ahmed, Saeed Amizadeh, Mikhail Bilenko, Rogan Carr, Wei-Sheng Chin, Yael Dekel, Xavier Dupre, Vadim Eksarevskiy, Senja Filipi, Tom Finley, Abhishek Goswami, Monte Hoover, Scott Inglis, Matteo Interlandi, Najeeb Kazmi, Gleb Krivosheev, Pete Luferenko, Ivan Matantsev, Sergiy Matusevych, Shahab Moradi, Gani Nazirov, Justin Ormont, Gal Oshri, Artidoro Pagnoni, Jignesh Parmar, Prabhat Roy, Mohammad Zeeshan Siddiqui, Markus Weimer, Shauheen Zahirazami, and Yiwen Zhu. 2019. Machine Learning at Microsoft with ML.NET. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 10.1145\/3292500.3330667"},{"key":"e_1_2_1_6_1","unstructured":"Alibaba. 2023. FeatHub - A stream-batch unified feature store for real-time machine learning. https:\/\/github.com\/alibaba\/feathub"},{"key":"e_1_2_1_7_1","unstructured":"Alibaba FeatHub. 2023. Fraud Detection. https:\/\/github.com\/alibaba\/feathub\/blob\/master\/docs\/examples\/fraud_detection.ipynb"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1017\/S0962492900002804"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.14778\/3446095.3446102"},{"key":"e_1_2_1_11_1","volume-title":"TVM: An Automated End-to-End Optimizing Compiler for Deep Learning","author":"Chen Tianqi","year":"2018","unstructured":"Tianqi Chen, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Yan, Meghan Cowan, Haichen Shen, Leyuan Wang, Yuwei Hu, Luis Ceze, Carlos Guestrin, and Arvind Krishnamurthy. 2018. TVM: An Automated End-to-End Optimizing Compiler for Deep Learning. Cornell University - arXiv,Cornell University - arXiv (Feb 2018)."},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3511299"},{"key":"e_1_2_1_13_1","unstructured":"ClickHouse. 2023. Fast Open-Source OLAP DBMS - ClickHouse. https:\/\/clickhouse.com\/"},{"key":"e_1_2_1_14_1","volume-title":"Clipper: A Low-Latency Online Prediction Serving System. arXiv: Distributed, Parallel, and Cluster Computing,arXiv: Distributed, Parallel, and Cluster Computing (Dec","author":"Crankshaw Daniel","year":"2016","unstructured":"Daniel Crankshaw, Xin Wang, Giulio Zhou, MichaelJ. Franklin, JosephE. Gonzalez, and Ion Stoica. 2016. Clipper: A Low-Latency Online Prediction Serving System. arXiv: Distributed, Parallel, and Cluster Computing,arXiv: Distributed, Parallel, and Cluster Computing (Dec 2016)."},{"key":"e_1_2_1_15_1","unstructured":"Dasmehdixtr. 2020. Binary Classification of Induction Motor Fault | Kaggle. https:\/\/www.kaggle.com\/code\/dasmehdixtr\/binary-classification-of-induction-motor-fault"},{"key":"e_1_2_1_16_1","unstructured":"Databricks Inc. 2023. The Data and AI Company - Databricks. https:\/\/www.databricks.com\/"},{"key":"e_1_2_1_17_1","unstructured":"Databricks Inc. 2023. feature-store-taxi-example - Databricks. https:\/\/docs.gcp.databricks.com\/_extras\/notebooks\/source\/machine-learning\/feature-store-taxi-example.html"},{"key":"e_1_2_1_18_1","unstructured":"etc David Gagnon Maggie. 2023. Predict Student Performance from Game Play. https:\/\/kaggle.com\/competitions\/predict-student-performance-from-game-play"},{"key":"e_1_2_1_19_1","volume-title":"Proceedings of the 2016 International Conference on Management of Data. 10","author":"Ding Bolin","year":"2016","unstructured":"Bolin Ding, Silu Huang, Surajit Chaudhuri, Kaushik Chakrabarti, and Chi Wang. 2016. Sample + Seek: Approximating Aggregates with Distribution Precision Guarantee. 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Feathr - A scalable unified data and AI engineering platform for enterprise. https:\/\/github.com\/feathr-ai\/feathr"},{"key":"e_1_2_1_25_1","volume-title":"Discrete Mathematics & Theoretical Computer Science DMTCS Proceedings","volume":"3545","author":"Flajolet Philippe","year":"2007","unstructured":"Philippe Flajolet, \u00c9ric Fusy, Olivier Gandouet, and Fr\u00e9d\u00e9ric Meunier. 2007. HyperLogLog: The Analysis of a near-Optimal Cardinality Estimation Algorithm. Discrete Mathematics & Theoretical Computer Science DMTCS Proceedings vol. AH,..., Proceedings (Jan. 2007), 3545. 10.46298\/dmtcs.3545"},{"key":"e_1_2_1_26_1","unstructured":"Google Cloud. 2023. Innovate faster with enterprise-ready generative AI. https:\/\/cloud.google.com\/vertex-ai"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","unstructured":"Gilad Gressel Niranjan Hegde Archana Sreekumar Rishikumar Radhakrishnan Kalyani Harikumar Anjali S. and Krishnashree Achuthan. 2023. Feature Importance Guided Attack: A Model Agnostic Adversarial Attack. arXiv:2106.14815 [cs] 10.48550\/arXiv.2106.14815","DOI":"10.48550\/arXiv.2106.14815"},{"key":"e_1_2_1_28_1","unstructured":"L\u00e9o Grinsztajn Edouard Oyallon and Ga\u00ebl Varoquaux. 2022. Why do tree-based models still outperform deep learning on tabular data? arXiv:2207.08815 [cs.LG]"},{"key":"e_1_2_1_29_1","unstructured":"Gusthema. 2023. Student Performance w\/ Random Forests. https:\/\/www.kaggle.com\/code\/cdeotte\/random-forest-baseline-0-664\/notebook"},{"key":"e_1_2_1_30_1","unstructured":"Gusthema. 2023. Student Performance w\/ TensorFlow Decision Forests. https:\/\/www.kaggle.com\/code\/gusthema\/student-performance-w-tensorflow-decision-forests"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.14778\/3551793.3551833"},{"key":"e_1_2_1_32_1","volume-title":"Online Aggregation. In Proceedings of the 1997 ACM SIGMOD International Conference on Management of Data (SIGMOD '97)","author":"Hellerstein Joseph M.","unstructured":"Joseph M. Hellerstein, Peter J. Haas, and Helen J. Wang. 1997. Online Aggregation. In Proceedings of the 1997 ACM SIGMOD International Conference on Management of Data (SIGMOD '97). Association for Computing Machinery, New York, NY, USA, 171--182. 10.1145\/253260.253291"},{"key":"e_1_2_1_33_1","volume-title":"[n.d.]. Computer Architecture","author":"Hennessy John L.","unstructured":"John L. Hennessy and David A. Patterson. [n.d.]. Computer Architecture, Fifth Edition: A Quantitative Approach (5 ed.). Morgan Kaufmann Publishers Inc."},{"key":"e_1_2_1_34_1","volume-title":"DeepDB: Learn from Data, not from Queries! arXiv: Databases,arXiv: Databases (Sep","author":"Hilprecht Benjamin","year":"2019","unstructured":"Benjamin Hilprecht, Andreas Schmidt, Moritz Kulessa, Alejandro Molina, Kristian Kersting, and Carsten Binnig. 2019. DeepDB: Learn from Data, not from Queries! arXiv: Databases,arXiv: Databases (Sep 2019)."},{"key":"e_1_2_1_35_1","unstructured":"Hopsworks. 2023. Hopsworks - Batch and Real-time ML Platform. https:\/\/www.hopsworks.ai"},{"key":"e_1_2_1_36_1","volume-title":"JoinBoost: Grow Trees Over Normalized Data Using Only SQL. (Jul","author":"Huang Zezhou","year":"2023","unstructured":"Zezhou Huang, Rathijit Sen, Jiaxiang Liu, and Eugene Wu. 2023. JoinBoost: Grow Trees Over Normalized Data Using Only SQL. (Jul 2023)."},{"key":"e_1_2_1_37_1","unstructured":"JS. 2023. Forex tick data huge database since april 2020 | Kaggle. https:\/\/www.kaggle.com\/datasets\/joseserrat\/forex-april-2020-to-june-2021-tick-data"},{"key":"e_1_2_1_38_1","volume-title":"Kaggle: Your Machine Learning and Data Science Community. https:\/\/www.kaggle.com\/","year":"2023","unstructured":"Kaggle. 2023. 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Conference on Innovative Data Systems Research,Conference on Innovative Data Systems Research (Jan","author":"Karanasos Konstantinos","year":"2019","unstructured":"Konstantinos Karanasos, Matteo Interlandi, Doris Xin, Fotis Psallidas, Rathijit Sen, Kwanghyun Park, Ivan Popivanov, Supun Nakandala, Subru Krishnan, Markus Weimer, Yuan Yu, Raghu Ramakrishnan, and Carlo Curino. 2019. Extending Relational Query Processing with ML Inference. Conference on Innovative Data Systems Research,Conference on Innovative Data Systems Research (Jan 2019)."},{"key":"e_1_2_1_42_1","unstructured":"Kengle. 2023. Predict Charge Time of Battery. https:\/\/www.kaggle.com\/code\/kenggle\/nasa-battery-life-prediction-dataset-cleaning"},{"key":"e_1_2_1_43_1","unstructured":"Kengle. 2023. RUL Prediction of Turbofan Engine. https:\/\/www.kaggle.com\/kenggle\/rul-prediction-with-lgbm-on-turbofan-dataset"},{"key":"e_1_2_1_44_1","volume-title":"Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference","author":"Kraft Peter","year":"2019","unstructured":"Peter Kraft, Daniel Kang, Deepak Narayanan, Shoumik Palkar, Peter Bailis, and Matei Zaharia. 2019. Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference. Cornell University - arXiv,Cornell University - arXiv (Jun 2019)."},{"key":"e_1_2_1_45_1","volume-title":"Everest: A Top-K Deep Video Analytics System. In Proceedings of the 2022 International Conference on Management of Data","author":"Lai Ziliang","year":"2022","unstructured":"Ziliang Lai, Chris Liu, Chenxia Han, Pengfei Zhang, Eric Lo, and Ben Kao. [n.d.]. Everest: A Top-K Deep Video Analytics System. In Proceedings of the 2022 International Conference on Management of Data (New York, NY, USA, 2022-06-11) (SIGMOD '22). Association for Computing Machinery, 2357--2360."},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00158-008-0234-7"},{"key":"e_1_2_1_47_1","volume-title":"Pretzel: opening the black box of machine learning prediction serving systems. Operating Systems Design and Implementation,Operating Systems Design and Implementation (Oct","author":"Lee Yunseong","year":"2018","unstructured":"Yunseong Lee, Alberto Scolari, Byung-Gon Chun, MarcoD. Santambrogio, Markus Weimer, and Matteo Interlandi. 2018. Pretzel: opening the black box of machine learning prediction serving systems. Operating Systems Design and Implementation,Operating Systems Design and Implementation (Oct 2018)."},{"key":"e_1_2_1_48_1","volume-title":"Proceedings of the 2016 International Conference on Management of Data. 10","author":"Li Feifei","year":"2016","unstructured":"Feifei Li, Bin Wu, Ke Yi, and Zhuoyue Zhao. 2016. Wander Join: Online Aggregation via Random Walks. In Proceedings of the 2016 International Conference on Management of Data. 10.1145\/2882903.2915235"},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1007\/s41019-018-0074-4"},{"key":"e_1_2_1_50_1","volume-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS'17)","author":"Scott","unstructured":"Scott M. Lundberg and Su-In Lee. 2017. A Unified Approach to Interpreting Model Predictions. In Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS'17). Curran Associates Inc., Red Hook, NY, USA, 4768--4777."},{"key":"e_1_2_1_51_1","volume-title":"Proceedings of the 2019 International Conference on Management of Data. 10","author":"Ma Qingzhi","year":"2019","unstructured":"Qingzhi Ma and Peter Triantafillou. 2019. DBEst: Revisiting Approximate Query Processing Engines with Machine Learning Models. In Proceedings of the 2019 International Conference on Management of Data. 10.1145\/3299869.3324958"},{"key":"e_1_2_1_52_1","unstructured":"Christoph Molnar. 2023. Interpretable Machine Learning. Online. https:\/\/christophm.github.io\/interpretable-ml-book\/"},{"key":"e_1_2_1_53_1","volume-title":"A Handbook for Building an Approximate Query Engine","author":"Mozafari Barzan","year":"2015","unstructured":"Barzan Mozafari and Ning Niu. 2015. A Handbook for Building an Approximate Query Engine. IEEE Data Eng. Bull. (2015)."},{"key":"e_1_2_1_54_1","volume-title":"14th USENIX Symposium on Operating Systems Design and Implementation (OSDI 20)","author":"Nakandala Supun","year":"2020","unstructured":"Supun Nakandala, Karla Saur, Gyeong-In Yu, Konstantinos Karanasos, Carlo Curino, Markus Weimer, and Matteo Interlandi. 2020. A tensor compiler for unified machine learning prediction serving. In 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI 20). 899--917."},{"key":"e_1_2_1_55_1","unstructured":"Nikhil Kohil. 2020. Stock Prediction using Linear Regression - Starter | Kaggle. https:\/\/www.kaggle.com\/code\/nikhilkohli\/stock-prediction-using-linear-regression-starter"},{"key":"e_1_2_1_56_1","volume-title":"Proceedings of the VLDB Endowment (May","author":"Palkar Shoumik","year":"2018","unstructured":"Shoumik Palkar, James Thomas, Deepak Narayanan, Pratiksha Thaker, Rahul Palamuttam, Parimajan Negi, Anil Shanbhag, Malte Schwarzkopf, Holger Pirk, Saman Amarasinghe, Samuel Madden, and Matei Zaharia. 2018. Evaluating end-to-end optimization for data analytics applications in weld. Proceedings of the VLDB Endowment (May 2018), 1002--1015. 10.14778\/3213880.3213890"},{"key":"e_1_2_1_57_1","volume-title":"Proceedings of the 2022 International Conference on Management of Data. 10","author":"Park Kwanghyun","year":"2022","unstructured":"Kwanghyun Park, Karla Saur, Dalitso Banda, Rathijit Sen, Matteo Interlandi, and Konstantinos Karanasos. 2022. End-to-end Optimization of Machine Learning Prediction Queries. In Proceedings of the 2022 International Conference on Management of Data. 10.1145\/3514221.3526141"},{"key":"e_1_2_1_58_1","volume-title":"Proceedings of the 2018 International Conference on Management of Data. 10","author":"Park Yongjoo","year":"2018","unstructured":"Yongjoo Park, Barzan Mozafari, Joseph Sorenson, and Junhao Wang. 2018. VerdictDB: Universalizing Approximate Query Processing. In Proceedings of the 2018 International Conference on Management of Data. 10.1145\/3183713.3196905"},{"key":"e_1_2_1_59_1","volume-title":"Proceedings of the 2017 ACM International Conference on Management of Data. 10","author":"Park Yongjoo","year":"2017","unstructured":"Yongjoo Park, Ahmad Shahab Tajik, Michael Cafarella, and Barzan Mozafari. 2017. Database Learning: Toward a Database that Becomes Smarter Every Time. In Proceedings of the 2017 ACM International Conference on Management of Data. 10.1145\/3035918.3064013"},{"key":"e_1_2_1_60_1","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa F.","year":"2011","unstructured":"F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12 (2011), 2825--2830.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_2_1_61_1","volume-title":"Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data","author":"Pol A.","year":"2005","unstructured":"A. Pol and C. Jermaine. 2005. Relational confidence bounds are easy with the bootstrap. Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data (2005). 10.1145\/1066157.1066224"},{"key":"e_1_2_1_62_1","volume-title":"Carlo Curino, and Markus Weimer","author":"Psallidas Fotis","year":"2019","unstructured":"Fotis Psallidas, Yiwen Zhu, Bojan Karlas, Matteo Interlandi, Avrilia Floratou, Konstantinos Karanasos, Wentao Wu, Ce Zhang, Subru Krishnan, Carlo Curino, and Markus Weimer. 2019. Data Science through the looking glass and what we found there. arXiv: Learning,arXiv: Learning (Dec 2019)."},{"key":"e_1_2_1_63_1","volume-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, San Francisco California USA, 1135--1144","author":"Ribeiro Marco Tulio","year":"2016","unstructured":"Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016. \"Why Should I Trust You?\": Explaining the Predictions of Any Classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, San Francisco California USA, 1135--1144. 10.1145\/2939672.2939778"},{"key":"e_1_2_1_64_1","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence 32","author":"Ribeiro Marco Tulio","year":"2018","unstructured":"Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2018. Anchors: High-Precision Model-Agnostic Explanations. Proceedings of the AAAI Conference on Artificial Intelligence 32, 1 (April 2018). 10.1609\/aaai.v32i1.11491"},{"key":"e_1_2_1_65_1","unstructured":"Mathieu Rouaud. [n.d.]. Probability Statistics and Estimation: Propagation of Uncertainties in Experimental Measurement. https:\/\/www.lulu.com\/shop\/mathieu-rouaud\/probability-statistics-and-estimation-propagation-of-uncertainties-in-experimental-measurement\/paperback\/product-1kwvyjky.html."},{"key":"e_1_2_1_66_1","volume-title":"https:\/\/www.infoworld.com\/article\/3575071\/amazon-google-and-microsoft-take-their-clouds-to-the-edge.html","author":"Sacolick Issac","year":"2020","unstructured":"Issac Sacolick. 2020. Amazon, Google, and Microsoft take their clouds to the edge. (2020). https:\/\/www.infoworld.com\/article\/3575071\/amazon-google-and-microsoft-take-their-clouds-to-the-edge.html"},{"key":"e_1_2_1_67_1","unstructured":"B. Saha and K. Goebel. 2007. 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