In the last 5+ years, researchers and the industry have been working hard to adopt MLOps (Machine Learning Operations) to maximize production. The current literature on MLOps is still mostly disconnected and sporadic (Testi et al., 2022). This study conducts mixed-method research, including a literature review, survey questionnaires, and expert interviews to address this gap. The researcher provides an aggregated overview of the necessary principles, components, roles, and the associated architecture and workflows resulting from these investigations. Furthermore, this research furnishes a definition of MLOps and addresses open challenges in the field. Finally, this work proposes a MLOps pipeline to implement product recommendations on the e-commerce platform to guide ML researchers and practitioners who want to automate and operate their ML products.