Data Engineering and MLOps: Building Scalable AI Solutions

Abstract

Due to the continuous nimble evolution of Artificial Intelligence (AI), there’s a need to develop good data engineering and scalability plans. This research focuses on how MLOps concepts are implemented to optimize and scale the deployment of AI solutions in data engineering. Based on a literature review with theoretical background and multiple case studies of leading organizations and practices, the research explores how MLOps unburdens data processing, automates data preparation and model deployment, and consequently guarantees machine learning models' continuous integration and delivery. The research evidence suggests that by embracing MLOps, the Data Engineers and Machine learning personnel can easily enhance AI solutions' performance, reliability, and scalability. The work reveals success stories and numerous recommendations for companies interested in developing large-scale AI solutions. The implications for practice are discussed, such as applying MLOps, which may result in better AI implementation and keep optimization at a desired level in the long run.