Machine learning, cloud computing, and synthetic biology


Dr. Afnizanfaizal Abdullah

Machine learning requires access to large amounts of data from various sources and formats. Leveraging a data repository to store the necessary information for empowering machine learning workloads to enable predictive analysis on a specific domain of interests. Therefore, designing and developing an infrastructure that able to accommodate useful machine learning algorithms in accessing this enormous information within a scalable computational resource is majorly important. This project is aimed to come out with an emerging machine learning–as–a–service (MLaaS) platform that is used to provide predictive analysis services in large and various genomic data. This product is expected to serve as a platform that enables prospective research and development in genomic analysis, which is useful in providing an environment to accessing large and various data swiftly, as well as able to deliver easier predictive analytic mechanisms to researchers, engineers, and industries in exploring new applications in healthcare and wellness.