Experimentation, deployment and monitoring Machine Learning models: Approaches for applying MLOps

In recent years, especially since 2010, Data Science has proven to be a fundamental discipline and a support tool for the industry to improve its decision-making supported by data. With the increased relevance of this area, the challenges of publishing the developed models into production to deliver the proposed value to end-users have become more prominent To address these challenges, the MLOps discipline has proven to be a promising approach, enabling the automation and governance of the processes of experimenting, publishing and monitoring Machine Learning models. The creation of MLOps pipelines is one of the main strategies to ensure the effectiveness and efficiency of these processes. This work is expected to contribute to the advancement of AI, promoting more efficient and transparent methodologies for end-to-end Machine Learning project development, looking for to answer the investigative question "What are the main challenges faced by companies when publishing Machine Learning models into production, and how can the discipline of MLOps helps overcome them?" and more specific questions like "Why is it necessary to carry out continuous monitoring throughout the entire development lifecycle of machine learning models?" and "What are the essential steps to ensure an automated, efficient, and secure environment for publishing machine learning models?". The remainder of the paper is organised as follow: in section 2 - MLOps pipeline, which explains the concepts and challenges of MLOps pipelines, in section 3 - Application and Case Study, applications and the benefits of implementing a solution with the stages of experimentation, publication and monitoring and three case studies from different fields of the industry that benefited from the implementation of MLOps are presented, and, in section 4 - Conclusion, the views of each of the three major areas explored are exposed.