The article explores the concept of MLOps, which stands for machine learning operations, a framework aimed at improving the management, deployment, and monitoring of machine learning models by integrating various collaborative processes. It highlights the challenges faced by machine learning (ML) teams, such as lengthy deployment times and abandoned experiments, and discusses how MLOps can help streamline these processes by borrowing principles from DevOps. MLOps enhances productivity and collaboration across different teams, ensuring that models are reliable and explainable while aligning with business objectives and regulatory requirements. The framework emphasizes the importance of explainability in AI, reducing model bias, and ensuring robust AI governance through well-documented and automated workflows. MLOps consists of stages that include data preparation, model development, validation, deployment, and performance monitoring, which help in handling issues like data and concept drift. The article also mentions Comet's platform as a tool to support these stages, offering solutions for managing and optimizing ML models throughout their lifecycle.