The Thesis Objective is to study and test different Machine Learning Algorithms suitable for Predictive Maintenance. Posteriorly, joining forces with Zirak’s ML Backend architecture, to develop a prototype of a Predictive Maintenance Framework for Real Case Implementation. This framework should have modular Architecture that allows fast set up and deployment, high scalability and adaptability, also simple maintenance, in order to be effectively flexible against the variety of use cases in the Industry.