Innovative Data Engineering Approaches for Scalable Artificial Intelligence Solutions
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The growth of AI technologies has resulted in increased need for more data engineering to meet the challenge presented by large volumes of data and the need for systems that can provide solutions for complex computational problems. In this work, new approaches to creating scalable AI infrastructures are proposed, which can help overcome the data acquisition and storage issues, as well as the challenge of real-time computational processing. To address the problem of efficient training and deployment of AI models, we use cloud architectures, distributed data processing frameworks, and machine learning optimization. Finally, by means of case-study and performance evaluations, we exemplify how these methodologies are successfully applied to reduce processing times by several orders of magnitude, increase data throughput rates and optimise the scalability of large systems. This paper shows that by adopting current best practices in data engineering, one is well-placed to significantly speed up the development of AI models as well as contain costs. These has great potential of revolutionizing sectors for example; health, finance and autonomous systems opening up other opportunities for AI in the future.
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