Optimizing Movie Recommendations with MLOps in AWS

Patil, Priyal Narendra (2024) Optimizing Movie Recommendations with MLOps in AWS. Masters thesis, Dublin, National College of Ireland.

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Abstract

In the age of streaming services and online content, there has been a significant increase in the need for personalized movie suggestions. This has led to the creation of complex recommendation systems capable of managing large amounts of data and quickly adapting to evolving user preferences. This study explores enhancing movie recommendation systems by incorporating Machine Learning Operations (MLOps) in Amazon Web Services (AWS) environments. The research focuses on the increasing demand for efficient and easily expandable recommendation systems due to the growth of digital content. Conventional machine learning methods frequently encounter difficulties when being implemented in real-world situations, especially when dealing with vast amounts of data and adjusting to user behavior. The Project highlights the significance of operational elements when it comes to implementing and upkeeping machine learning models in a production environment. Utilizing AWS’s strong cloud infrastructure, the project seeks to develop a streamlined MLOps pipeline to enable ongoing integration, delivery, and monitoring of machine learning models. This method guarantees that the recommendation system will be able to grow, stay dependable, and respond quickly, leading to enhanced user happiness. The project utilizes different AWS services such as SageMaker for constructing models, Lambda for executing serverless functions, and S3 for storing data, in order to establish a smooth, automated pipeline which results emphasize how MLOps can improve the performance and scalability of movie recommendation systems, offering valuable insights for future applications in this area.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Kumar Sharma, Jitendra UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Cloud computing H Social Sciences > HD Industries. Land use. Labor > Specific Industries > Film Industry Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Cloud Computing
Depositing User:	Ciara O'Brien
Date Deposited:	04 Jul 2025 10:17
Last Modified:	04 Jul 2025 10:17
URI:	https://norma.ncirl.ie/id/eprint/8047

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