The Practice of Implementing ML Service into an Internet Business Application

Pavels Osipovs

Abstract


Currently, there are a large number of articles describing the theoretical aspects of development in the field of machine learning. However, the experience of their practical application in real systems is described much less often. Basically, authors describe the efficiency, accuracy, and other performance metrics of the resulting solution, but everything stops at the prototype stage. At the same time, how the trained model will behave not on test data, but in real conditions, can be very different from the indicators obtained at the development stage. This article describes the experience of the implementation and real use of a classification service based on machine learning techniques.


Keywords:

Machine learning; machine learning for business; REST service; text classification; WEB API

Full Text:

PDF

References


J. Plisson, N. Lavrac, and D. Mladenic, “A rule based approach to word lemmatization,” Proceedings of IS04, Ljubljana, Slovenia, vol. 3, 2004.

F. Pedregosa et al., “Scikit-learn: Machine learning in Python,” Journal of Machine Learning Research, vol. 12, no. 85, pp. 2825–2830, 2011.

F. Lanubile, C. Ebert, R. Prikladnicki, and A. Vizcaíno, “Collaboration tools for global software engineering,” IEEE software, vol. 27, no. 2, pp. 52–55, 2010. https://doi.org/10.1109/MS.2010.39

“Naive Bayes classifier for multinomial models”. [Online]. Available: https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.MultinomialNB.html. Accessed on: Sep. 12, 2021.

“Decision tree classifier”. [Online]. Available: https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html. Accessed on: Sep. 12, 2021.

“Logistic regression CV (logit, MaxEnt) classifier”. [Online]. Available: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegressionCV.html#sklearn.linear_model.LogisticRegressionCV. Accessed on: Sep. 12, 2021.

“Classifier implementing the k-nearest neighbors vote”. [Online]. Available: https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html. Accessed on: Sep. 12, 2021.

“Linear classifiers (SVM, logistic regression, etc.) with SGD training”. [Online]. Available: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html#sklearn.linear_model.SGDClassifier. Accessed on: Sep. 12, 2021.

S. Diab, “Optimizing stochastic gradient descent in text classification based on fine-tuning hyper-parameters approach. A case study on automatic classification of global terrorist attacks,” International Journal of Computer Science and Information Security (IJCSIS), vol. 16, no. 12, pp. 155–160, Dec. 2018.

“C-Support vector classification”. [Online]. Available: https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html. Accessed on: Sep. 12, 2021.

“One-vs-the-rest (OvR) multiclass strategy”. [Online]. Available: https://scikit-learn.org/stable/modules/generated/sklearn.multiclass.OneVsRestClassifier.html. Accessed on: Sep. 12, 2021.

“Supervisor: A process control system”. [Online]. Available: http://supervisord.org/. Accessed on: Sep. 12, 2021.

C. Nedelcu, Nginx HTTP Server. Packt Publishing, 2013.

H. Wenhui et al., “Study on REST API test model supporting web service integration,” 2017 IEEE 3rd International Conference on Big Data Security on Cloud (bigdatasecurity), IEEE International Conference on High Performance and Smart Computing (hpsc), and IEEE International Conference on Intelligent Data and Security (ids), Beijing, China, May 2017, pp. 133–138. https://doi.org/10.1109/BigDataSecurity.2017.35

D. Rahmel, “Testing a site with ApacheBench, JMeter, and Selenium,” Advanced Joomla!. Apress, Berkeley, CA, 2013, pp. 211–247. https://doi.org/10.1007/978-1-4302-1629-2_9

L. Richard et al., “Potassium: penetration testing as a service,” Proceedings of the Sixth ACM Symposium on Cloud Computing, Aug. 2015, pp. 30–42. https://doi.org/10.1145/2806777.2806935




DOI: 10.7250/itms-2021-0002

Refbacks

  • There are currently no refbacks.


Copyright (c) 2021 Pāvels Osipovs

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.