What is MLOps?

MLOps, short for Machine Learning Operations, is a practice that focuses on managing and streamlining the lifecycle of machine learning models, from development and testing to deployment and monitoring in production environments. It aims to bridge the gap between data science and operations teams, similar to how DevOps connects developers and operations, facilitating collaboration among data engineers, data scientists, and MLOps engineers. MLOps incorporates best practices, such as continuous integration and delivery, data version control, and robust security measures, to ensure the effective deployment of machine learning models while addressing challenges like model accuracy, robustness, and bias. Platforms like New Relic support MLOps by offering tools for monitoring and optimizing machine learning applications, providing full observability across the MLOps lifecycle and promoting a seamless integration of MLOps best practices into production workflows. As organizations increasingly rely on artificial intelligence to achieve business goals, MLOps becomes essential for ensuring reliable, scalable, and efficient machine learning systems, ultimately helping companies unlock new revenue sources, save time, and reduce operational costs.