The rapid growth of artificial intelligence (AI) and machine learning (ML) solutions has created a need to develop, deploy and maintain AI/ML those to production reliably and efficiently. MLOps (Machine Learning Operations) framework is a collection of tools and practices that aims to address this challenge. Within the MLOps framework, a concept called the feature store is introduced, serving as a central repository responsible for storing, managing, and facilitating the sharing and reuse of extracted features derived from raw data.
This study gives first an overview of the MLOps framework and delves deeper into feature engineering and feature data management, and explores the challenges related to these processes. Further, feature stores are presented, what they exactly are and what benefits do they introduce to organizations and companies developing ML solutions. The study also reviews some of the currently popular feature store tools.
The primary goal of this study is to provide recommendations for organizations to leverage feature stores as a solution to the challenges they encounter in managing feature data currently. Through an analysis of the current state-of-the-art and a comprehensive study of organizations' practices and challenges, this research presents key insights into the benefits of feature stores in the context of MLOps.
Overall, the thesis highlights the potential of feature stores as a valuable tool for organizations seeking to optimize their ML practices and achieve a competitive advantage in today's data-driven landscape. The research aims to explore and gather practitioners' experiences and opinions on the aforementioned topics through interviews conducted with experts from Finnish organizations.
