Advertisement

Bridging the Gap Between Research and Production with CODE

  • Yiping JinEmail author
  • Dittaya Wanvarie
  • Phu T. V. Le
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11441)

Abstract

Despite the ever-increasing enthusiasm from the industry, artificial intelligence or machine learning is a much-hyped area where the results tend to be exaggerated or misunderstood. Many novel models proposed in research papers never end up being deployed to production. The goal of this paper is to highlight four important aspects which are often neglected in real-world machine learning projects, namely Communication, Objectives, Deliverables, Evaluations (CODE). By carefully considering these aspects, we can avoid common pitfalls and carry out a smoother technology transfer to real-world applications. We draw from a priori experiences and mistakes while building a real-world online advertising platform powered by machine learning technology, aiming to provide general guidelines for translating ML research results to successful industry projects.

Keywords

Machine learning Project management Online advertising Real-time bidding 

Notes

Acknowledgement

The first author is supported the scholarship from “The 100\(^{th}\) Anniversary Chulalongkorn University Fund for Doctoral Scholarship” and also “The 90\(^{th}\) Anniversary Chulalongkorn University Fund (Ratchadaphiseksomphot Endowment Fund)”. We would like to thank Assoc. Prof. Peraphon Sophatsathit and the anonymous reviewers for their careful reading and their insightful suggestions.

References

  1. 1.
    Bagherjeiran, A., Tang, R., Zhang, Z., Hatch, A., Ratnaparkhi, A., Parekh, R.: Adaptive targeting for finding look-alike users. US Patent 9,087,332, 21 July 2015Google Scholar
  2. 2.
    Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)
  3. 3.
    Barker, J., Watanabe, S., Vincent, E., Trmal, J.: The fifth ‘CHiME’ speech separation and recognition challenge: dataset, task and baselines. arXiv preprint arXiv:1803.10609 (2018)
  4. 4.
    Boyko, A., Harchaoui, Z., Nedelec, T., Perchet, V.: A protocol to reduce bias and variance in head-to-head tests. Criteo Internal Report (2015)Google Scholar
  5. 5.
    Brooks, F.P.: The mythical man-month. Datamation 20(12), 44–52 (1974)Google Scholar
  6. 6.
    Enam, S.Z.: Why is machine learning ‘hard’? (2016). http://ai.stanford.edu/~zayd/why-is-machine-learning-hard.html. Accessed 10 Sept 2018
  7. 7.
    Goodfellow, I., Bengio, Y., Courville, A., Bengio, Y.: Deep Learning, vol. 1. MIT press, Cambridge (2016)zbMATHGoogle Scholar
  8. 8.
    Hermann, J., Del Balso, M.: Scaling machine learning at uber with michelangelo (2018). https://eng.uber.com/scaling-michelangelo/
  9. 9.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  10. 10.
    Jin, Y., Wanvarie, D., Le, P.: Combining lightly-supervised text classification models for accurate contextual advertising. In: Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), vol. 1, pp. 545–554 (2017)Google Scholar
  11. 11.
    Juan, Y., Lefortier, D., Chapelle, O.: Field-aware factorization machines in a real-world online advertising system. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 680–688. International World Wide Web Conferences Steering Committee (2017)Google Scholar
  12. 12.
    Modi, A.N., et al.: TFX: a tensorflow-based production-scale machine learning platform. In: KDD 2017 (2017)Google Scholar
  13. 13.
    Ng, A.: AI transformation playbook: how to lead your company into the AI era (2018). https://landing.ai/ai-transformation-playbook/
  14. 14.
    Pappas, N., Popescu-Belis, A.: Multilingual hierarchical attention networks for document classification. arXiv preprint arXiv:1707.00896 (2017)
  15. 15.
    Perlich, C., Dalessandro, B., Hook, R., Stitelman, O., Raeder, T., Provost, F.: Bid optimizing and inventory scoring in targeted online advertising. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 804–812. ACM (2012)Google Scholar
  16. 16.
    Peterson, L.E.: K-nearest neighbor. Scholarpedia 4(2), 1883 (2009)CrossRefGoogle Scholar
  17. 17.
    Pfister, R., Janczyk, M.: Confidence intervals for two sample means: calculation, interpretation, and a few simple rules. Adv. Cogn. Psychol. 9(2), 74 (2013)CrossRefGoogle Scholar
  18. 18.
    Polyzotis, N., Roy, S., Whang, S.E., Zinkevich, M.: Data management challenges in production machine learning. In: Proceedings of the 2017 ACM International Conference on Management of Data, pp. 1723–1726. ACM (2017)Google Scholar
  19. 19.
    Qu, Y., et al.: Product-based neural networks for user response prediction. In: 2016 IEEE 16th International Conference on Data Mining (ICDM), pp. 1149–1154. IEEE (2016)Google Scholar
  20. 20.
    Raeder, T., Stitelman, O., Dalessandro, B., Perlich, C., Provost, F.: Design principles of massive, robust prediction systems. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1357–1365. ACM (2012)Google Scholar
  21. 21.
    Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. (IJCV) 115(3), 211–252 (2015).  https://doi.org/10.1007/s11263-015-0816-yMathSciNetCrossRefGoogle Scholar
  22. 22.
    Sculley, D., Phillips, T., Ebner, D., Chaudhary, V., Young, M.: Machine learning: the high-interest credit card of technical debt (2014)Google Scholar
  23. 23.
    Shearer, C.: The CRISP-DM model: the new blueprint for data mining. J. Data Warehous. 5(4), 13–22 (2000)Google Scholar
  24. 24.
    Shi, L., Mihalcea, R., Tian, M.: Cross language text classification by model translation and semi-supervised learning. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pp. 1057–1067. Association for Computational Linguistics (2010)Google Scholar
  25. 25.
    Sra, S., Nowozin, S., Wright, S.J.: Optimization for Machine Learning. MIT Press, Cambridge (2012)Google Scholar
  26. 26.
    Thomas, R.: What do machine learning practitioners actually do? (2018). http://www.fast.ai/2018/07/12/auto-ml-1/. Accessed 10 Sept 2018
  27. 27.
    Yuan, Y., Wang, F., Li, J., Qin, R.: A survey on real time bidding advertising. In: 2014 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI), pp. 418–423. IEEE (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Mathematics and Computer ScienceChulalongkorn UniversityBangkokThailand
  2. 2.Knorex Pte. Ltd.SingaporeSingapore

Personalised recommendations