As organizations increasingly adopt artificial intelligence and machine learning, they face significant challenges in production model deployment and maintenance. MLOps emerges as a methodology that integrates DevOps principles with machine learning requirements to streamline model lifecycle management through automation and standardization. In this thesis, we conducted a practical comparison between MLOps and DevOps approaches by implementing a cycling race prediction system, while theoretically analyzing other development methodologies. Our findings demonstrate that MLOps offers superior advantages for machine learning systems through enhanced automation, traceability, and reliability, though DevOps remains better suited for projects with infrequent model updates and limited computational resources. This research provides organizations with a valuable framework for optimizing their machine learning development processes.
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