Establishing an Efficient and Cost-Effective Infrastructure for Small and Medium Enterprises to Drive Data Science Projects from Prototype to Production

Abstract

Eric Schmidt, Google's Executive Chairman, perceptively remarked that the amount of data generated from the dawn of civilization until 2003, estimated at 5 Exabyte, now materializes every two days, underscoring the data-driven paradigm of our times. While big data unquestionably occupies a pivotal role in data science, its significance extends beyond sheer volume. Big data solutions prioritize data organization and preprocessing over extensive analysis. In recent years, Data Science has assumed a pivotal role across diverse industries, spanning agriculture, risk management, fraud detection, marketing optimization, and public policy. Within these domains, Data Science leverages machine learning, statistical analysis, data preparation, and predictive modeling to tackle multifaceted challenges and provide actionable. Currently, numerous enterprises are fervently pursuing data-driven models, necessitating transitions to cloud and Spark clusters. However, the lack of comprehensive expertise often leads to increased costs and suboptimal infrastructure outcomes. Cultivating a holistic understanding of contemporary technologies and their seamless integration with existing infrastructure is imperative. This holistic approach enables the establishment of an adaptive platform tailored to specific organizational needs, facilitating exploration and implementation of machine learning-based solutions. Attaining this goal hinges on meticulous examination of the advantages and limitations inherent in current big data technologies.
Through our research, we aspire to offer recommendations and construct a comprehensive infrastructure framework tailored to small and medium-sized enterprises (SMEs). This endeavor aims to bolster their capabilities in proficiently developing machine learning solutions, aligning them with the evolving landscape of data-driven innovation.