Josu Diaz-de-Arcaya

Data science projects represent a greater challenge than software engineering for organizations pursuing their adoption. The diverse stakeholders involved emphasize the need for a collaborative culture in organizations. This article aims to offer joint insights into the role of MLOps and AIOps methodologies for raising the success of data science projects in various fields, ranging from pure research to more traditional industries. We analyze the open issues, opportunities, and future trends organizations face when implementing MLOps and AIOps. Then, the frameworks and architectures that promote these paradigms are presented, as are the different fields in which they are being utilized. This systematic review was conducted using an automated procedure that identified 44,903 records, which were filtered down to 93 studies. These articles are meant to better clarify the problem at hand and highlight the future areas in both research and industry in which MLOPs and AIOps are thriving. Our findings indicate that AIOps flourish in challenging circumstances like those presented by 5G and 6G technologies, whereas MLOps is more prevalent in traditional industrial environments. The use of AIOps in certain stages of the ML lifecycle, such as deployment, remains underrepresented in scientific literature.