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Machine learning is a domain highly influenced by the rapid evolution of cloud computing and has reached a maturity point where a plethora of data processing capabilities is now widely available. The aim of the present study is to investigate the potential for building a common platform to support direct end-user application of machine learning algorithms across diverse scientific areas, emphasizing not only the suitability of appropriate tools, but also how results can be disseminated and utilized in a shared data environment. Three case studies are presented: i) quality evaluation metrics for tomographic image reconstruction in positron emission tomography (PET), ii) health impacts of surface UV radiation and iii) demographic determinants that influence the perception of fraud and corruption incidents within different industry sectors. Tests showed that commercially available cloud resources are over-sufficient to consolidate results from a variety of teams and applications and are able to contribute to the build-up of a valuable shared knowledge repository. The cloud service platform exploits machine learning models and helps automate the training and prediction process. The suggested approach makes optimization more efficient and supports the transition to a more sustainable global information environment by breaking knowledge silos.