In the early years of its life, the use of machine learning was limited to academic research, where it had the opportunity to evolve. In recent years the transition to the industrial world has taken place, and nowadays in any field especially in the largest companies machine learning is assuming an increasingly central role. This process comes with challenges: every technological evolution, which in some cases can also be disruptive, involves work and organizational adaptations. Moreover, gaps and differences demark the distance among how academic research and real-world companies work. The objective of machine learning operations (MLOps) is to shorten but also make more reliable the major phases that characterise the deployment and maintenance of machine learning model in production.