The significant improvements in machine learning today necessitate procedures and frameworks that are simple to configure by data scientists, ML researchers, and non-DevOps engineers, allowing them to rapidly develop ML models and address real-world challenges. This article provides an in-depth analysis of the architectural design of a holistic system that addresses the management of machine learning processes and deployment procedures. The proposed system has been designed to provide seamless configuration options and smooth integration with other solutions by enabling the rapid development of ML models. Its versatility makes it applicable in various scenarios, enhancing its usability across different domains. The effectiveness of the proposed solution has been validated through practical implementation in two distinct machine ML scenarios, both of which were integrated into European Union (EU) projects centered around the healthcare domain. The findings from these specific use cases have been documented and are presented as part of the empirical evidence supporting the viability and success of the solution.