Machine learning (ML) and Artificial Intelligence (AI) are now integral part of everyone’s daily life. However, quickly developing and operationalizing these ML models is still remains a key challenge for businesses. To address this, two concepts have emerged in the industry and academia: Automated Machine Learning (AutoML) and Machine Learning Operationalization (MLOps). While AutoML aims to assist in defining and designing the best ML model solution for business problems, whereas, MLOps focuses on the deployment, sustaining, and maintenance of these models. In this paper, we have not only performed literature review to analyze the current state, summarized the gaps, and shared the future perspective as well. But, also evaluated whether there is an interplay exists between these concepts (AutoML and MLOps). If so, what will it look like along with the potential benefits it can provide to the organizations in getting machine learning accessible to a larger variety of users, accelerating the development and implementation of machine learning models to increase the efficiency and reduce the total cost of ownership (TCO) of machine learning projects.