This chapter introduces crucial concepts of Cloud Computing and Federated Learning paradigms that are vital for a modern deployment of Trustworthy AI. First, the architecture and concepts for a Cloud computing system are introduced, highlighting its distinctive qualities such as scalability, elasticity, and on-demand resource provisioning. These attributes make Cloud platforms attractive for training and deploying models, yet they also introduce new challenges related to data security and privacy, particularly when handling sensitive information as the ones found in healthcare applications. To address these concerns on training, Federated Learning (FL) is also introduced in this chapter. FL enables collaborative training of AI models across multiple distributed datasets without directly sharing raw data. This approach mitigates the risks associated when sharing data towards a common model. Furthermore, we explore the critical aspects of model deployment as models need to be secured, protected and monitored when they are on production. In particular we explore the concept of local deployment of models to ensure that the model usage lies in a trusted environment.