In the current era of software engineering, where Machine Learning (ML) plays a crucial role in technological innovation, effective implementation of development and operations practices is essential. The DevSecOps (Development, Security, Operations) approach has gained popularity due to its ability to integrate security and quality into every stage of the software development lifecycle. However, in the specific context of Machine Learning, the need arises for a specialized approach that takes into account the peculiarities of the models and algorithms used. Machine Learning Operations (MLOps), despite their relative novelty, seek to establish a framework to characterize the ML development life cycle, decouple it from software development, and ensure quality attributes such as scalability, maintainability, and security. This paper focuses on the exploration and application of Machine Learning Operations (MLOps) in the specific context of developing and provisioning machine learning models.