Machine Learning as a Service Cloud Selection: An MCDM Approach for Optimal Decision Making

• Published in: Procedia computer science 2024, Vol.233, p.909-918
• Main Authors: Bhol, Seema Gupta, Mohanty, Satarupa, Pattnaik, Prasant Kumar
• Format: Article
• Language: English
• Subjects: Analytic Hierarchy Process (AHP); Machine Learning as a Service (MLaaS); Multi Criteria Decision making (MCDM); TOPSIS
• ISSN: 1877-0509; 1877-0509
• DOI: 10.1016/j.procs.2024.03.280

Machine Learning as a Service, MLaaS refers to cloud-based platforms that provide machine learning tools and infrastructure to users, allowing them to access and utilize machine learning capabilities without managing the underlying hardware and software. Cloud service providers offer scalable and flexible resources, enabling businesses to train and deploy machine learning models efficiently and cost-effectively. Multi-Criteria Decision making, MCDM helps individuals or organizations make choices when multiple criteria or objectives are involved. It aims to find the best alternative among a set of available options by considering various criteria or factors that are relevant to the decision. Machine learning as a service (MLaaS) and MCDM are two distinct concepts that can be combined to create powerful decision-making solutions. The present paper proposed an integrated approach based on the Analytic Hierarchy Process and TOPSIS method for choosing the best MLaaS cloud. Four alternatives for MLaaS cloud were analyzed based on seventeen shortlisted criteria. Based on the calculations, conclusions have been reached and potential study areas have been identified.