Machine learning (ML) models are a valuable tool for decision support in internet-delivered cognitive behavioral therapy (iCBT). However, while the literature extensively covers model development, a gap exists in the practical deployment of these models. This work proposes a novel system architecture to efficiently compose and deploy an ensemble of ML models tailored for iCBT in the cloud. We first establish system requirements and evaluation metrics based on the iCBT workflow and derive the system architecture based on these. We develop and implement a prototype of the system architecture for the composition and deployment of ML models in iCBT and validate the prototype with representative data through unit and integration tests. The results of the conceptual validation show that the prototype successfully facilitates the deployment of the models per the desired system requirements. Finally, we outline a path to a scalable deployment.