A malware can completely destroy the confidentiality, integrity, and availability of a system once it comes into contact with it, it poses a serious threat to computer security. Various mitigation and detection techniques have been developed over decades to address this problem. In order to deal with complex datasets that result in a high computational overhead and resource consumption, a new model that uses microservices to implement the random forest algorithm for malware detection and analysis has been proposed in this paper. The latter part of this paper discusses the overall microservice model and how it can lead to improved results overtime when the size of dataset provided increases by thousands. In conclusion a detailed difference between the micro- services-based model and the non-microserviced model has been presented by use of the acquired results gained during the application phase, the comparison details the parameters such as ROC curve, precision recall curve, bootstrapping resampling for AUC & a detailed feature selection of dataset to showcase the difference in values between the microservices model dataset and baseline model dataset.