The integration of machine learning into an existing system
Sikandar, Hafiz Muhammad Shahzad (2020)
Diplomityö
2020
School of Engineering Science, Tietotekniikka
Kaikki oikeudet pidätetään.
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2020040210176
https://urn.fi/URN:NBN:fi-fe2020040210176
Tiivistelmä
We have been living in the era, where development of first version of applications is quite easy and affordable. As the application grow the managing data becomes the problem for many systems. That’s develop attraction for mostly systems to go forward with integration of ML.
This thesis explores the various aspects related to integration of ML, starting from introduction about the ML methods, algorithms, and models. Furthermore, the detailed explanation about the process of integration the ML, modeling the architectures for ML has been followed up. The process for integrating the ML itself includes six main steps to follow, start with the study of conventional or existing system, selection ML method, selecting the process model, selecting tool for ML integration (depending upon the platform), implementation and deployment. The last and general part of integrating is to test the outcome and make continuous improvement.
Later, this thesis includes to explore the case studies. The selected two case studies emphasized the steps and factors needs to introduce the ML in the conventional systems. First case study, data parsing, text extraction and keywords extraction while clustering label ML technique has been implemented in second case study. The factors like stability, performance, flow of data, architecture, features, flexibility transparency and speed always influence the functionalities of the ML integration outcomes. Along with positive, there is always some risk and challenges while integrating ML like protecting data in term of security and privacy, getting the relevant data, maintaining the speed of ML system to increase the productivity of the system.
This thesis explores the various aspects related to integration of ML, starting from introduction about the ML methods, algorithms, and models. Furthermore, the detailed explanation about the process of integration the ML, modeling the architectures for ML has been followed up. The process for integrating the ML itself includes six main steps to follow, start with the study of conventional or existing system, selection ML method, selecting the process model, selecting tool for ML integration (depending upon the platform), implementation and deployment. The last and general part of integrating is to test the outcome and make continuous improvement.
Later, this thesis includes to explore the case studies. The selected two case studies emphasized the steps and factors needs to introduce the ML in the conventional systems. First case study, data parsing, text extraction and keywords extraction while clustering label ML technique has been implemented in second case study. The factors like stability, performance, flow of data, architecture, features, flexibility transparency and speed always influence the functionalities of the ML integration outcomes. Along with positive, there is always some risk and challenges while integrating ML like protecting data in term of security and privacy, getting the relevant data, maintaining the speed of ML system to increase the productivity of the system.