Use of Machine Learning for Continuous Improvement and Handling Multi-Dimensional Data in Service Sector

Abstract

Machine learning is known as a significant pattern of AI that gives an effective allowance to the software applications to become precise at forecasting outcomes without explicitly programmed in doing that. In addition, machine learning is important as this gives service sectors a suitable view of trends in “business operational patterns” and consumer behaviors. Service sectors are mainly known as the healthcare sectors, tourism sectors, and transportation sectors. In several developed countries, AI is maximizing labor productivity by more than 30% in the coming 15 years. The requirement of showing the usage of machine learning and the way it handles the multi-dimensional data have also been shown in this entire work. Machine learning shows some ways through that it helps in providing improvement to all the service sectors such as enhancing consumer analytics, giving rapid and effective assistance, providing effective personalization, identifying the fraud cases and also enhancing customer experiences. Though, in this research work it has been highlighted that, in terms of implementing ML in service sectors, service sectors are facing several challenges. Moreover, in terms of showing the effectiveness of ML two algorithms with flowcharts have been shown in this work. On the other hand, in this research work, a secondary data collection method has been utilized and a qualitative data analysis method has also been used in this research work. In addition, secondary data resources have been assembled from books, scholarly articles, journals, and newspapers. Index Terms : Machine learning, secondary data resources, AI, Service sectors.

TRiP: a transfer learning based rice disease phenotype recognition platform using SENet and microservices

“…Deployed on cloud infrastructure, MLaaS facilitates the task reasoning services ( Ribeiro et al, 2015 ). Integrating MLaaS with a microservices architecture enhances its advantages, offering scalable approach for developing and deploying machine learning applications ( Labreche et al., 2022 ; Lohit et al., 2022 ). This approach allows developers to integrate machine learning into their application architectures without the need to manage infrastructure or possess specialized skills ( Manley et al., 2022 ).…”