The Synergy of Data Engineering and Cloud Computing in the Era of Machine Learning and AI
DOI:
https://doi.org/10.60087/jklst.vol1.n1.p75Keywords:
Data Engineering, Cloud Computing, Machine Learning, Artificial Intelligence, Data Pipelines, ScalabilityAbstract
The integration of data engineering and cloud computing has become increasingly vital in harnessing the potential of machine learning (ML) and artificial intelligence (AI) technologies. This paper explores the symbiotic relationship between data engineering and cloud computing, elucidating how their synergy facilitates the development and deployment of ML and AI solutions. By leveraging the scalability, flexibility, and accessibility of cloud infrastructure, organizations can efficiently manage, process, and analyze vast amounts of data, thereby fueling the advancement of ML and AI initiatives. Furthermore, the convergence of data engineering techniques with cloud-based services enables seamless integration of disparate data sources, enhances data quality, and streamlines data pipelines, laying the groundwork for robust ML and AI models. This paper discusses key strategies, challenges, and opportunities associated with leveraging the combined power of data engineering and cloud computing to drive innovation and maximize the potential of ML and AI technologies.
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