Experimentation, deployment, and monitoring of machine learning models: How MLOps enhances AI productization

In recent years, Data Science has become increasingly relevant as a support tool for industry, profoundly impacting decision-making. With the advent of new technologies and tools, companies have begun to recognize the power of AI in addressing everyday use cases, especially in this decade. However, despite its relevance, many companies face significant obstacles to drive Machine Learning models to productive environments, and performing some processes manually can lead to errors, low reproducibility, and inefficiency. In this context, the MLOps discipline emerges as a solution to automate the life cycle of Machine Learning models, ranging from experimentation to monitoring in productive environments. This work explores how MLOps pipeline implementation can help mitigate or even eliminate many of these challenges by presenting three successful case studies, including notable application in the largest financial bank in Latin America.