Please use this identifier to cite or link to this item: http://thuvienso.dut.udn.vn/handle/DUT/6163
Title: Building a machine learning operations platform and data experimentation for Anti-money laundering detection
Authors: Than, Van Hong Son
Keywords: Building a machine learning;Data experimentation;Cloud computing
Issue Date: 2024
Publisher: Trường Đại học Bách khoa - Đại học Đà Nẵng
Abstract: 
With the robust development of cloud computing, the explosion of big data, and
the continuous advancement of science and technology, machine learning (ML) has
been and is being widely applied across various industries. The arising issue is how to
increase the performance and reliability of ML applications and minimize the go-tomarket time in the process of building and applying AI/ML.
As a result, a series of standards for operating and deploying ML projects have
gradually been formed, opening up a completely new direction for Machine Learning
Engineers and Data Scientists, known as Machine Learning Operations or MLOps. This
aims to accelerate the process of bringing ML products to production more quickly and
efficiently.
In this thesis, I have integrated the Machine Learning Operations (MLOps) into
Anti-Money Laundering (AML) detection enhances the ability of financial institutions
to identify and prevent money laundering activities. This approach involves the use of
machine learning models and automated processes to improve the efficiency and
accuracy of AML systems.
Description: 
41 tr.
URI: http://thuvienso.dut.udn.vn/handle/DUT/6163
Appears in Collections:DA.Khoa học dữ liệu - Trí tuệ nhân tạo

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