The aim of this thesis is to the design and implement a Machine Learning Operation (MLOps) model for large enterprise BigData and Machine Learning algorithms management on cloud. MLOps is the process that moves machine learning models into production environments. It unifies data collection, preprocessing, model training, evaluation, deployment, and retraining in a single process that teams can maintain and continuously monitor. Specifically, this work got started from a customer project in the Energy Sector with the need of using real data and actual machine learning algorithms provided by the customer. The data elaboration and model learning ask for a tailored strategy to exploit cloud services (Amazon Web - AWS) and guarantee security and scalability.