Machine Learning Operations (MLOps): A Comprehensive Study of Life Cycle, Implementation Strategies, and Platform Architecture

This paper presents a comprehensive study of Machine Learning Operations (MLOps) from multiple perspectives, including Bring Your Own Model (BYOM), model development using DataBricks and MLFlow, AutoML capabilities, drift detection mechanisms, and no-code platforms like DataRobot. The research focuses on the complete journey and life cycle of machine learning models from development to production deployment, rather than specific cloud platforms or tools. The study provides insights from a MLOps platform architect's perspective, examining various approaches to model industrialization and maintenance. Key findings include comparative analysis of different deployment strategies, evaluation of drift detection methodologies, and assessment of automated machine learning capabilities across platforms. The research demonstrates practical implementations using tools like Jupyter Notebook, Flask, ngrok, Docker, and enterprise platforms, providing a holistic view of the MLOps ecosystem. Results indicate that while no single approach fits all use cases, understanding the complete spectrum of available tools and methodologies is crucial for successful model deployment and maintenance in production environments.