Exploring the Open-Source Concept to Enhance the Learning and Exploration Curve of AI Architecture

The architecture of artificial intelligence has transformed the foundational principles behind the development of digital solutions across diverse fields, such as education, healthcare, and defense. This transformation is most evident in the processes of data analysis and the construction of predictive models for forecasting outcomes. Successfully implementing such architecture requires an understanding of the learning curve and the techniques for deploying AI frameworks. This learning phase, particularly during the pre-production stage of lifecycle development, often demands various resources, with three key pillars being crucial: data, algorithms, and digital tools or libraries. This study investigates into these three pillars through the perspective of "open-source." It utilizes two open-source datasets—one to develop an "analytical model" using distinct data analysis methods, and the other to create an AI "predictive model" based on the LSTM algorithm. Both models are built using open-source tools and frameworks. The study's findings provide a foundational example, showcasing how leveraging open-source datasets, AI models, and libraries or frameworks can effectively: (1) expedite the learning process or exploration necessary for adopting AI architecture as a solution, and (2) support the implementation of AI solutions across various industries as automated approaches. Future research is also recommended