MLOps is a set of practices that streamlines the lifecycle of machine learning models from development and testing to deployment and monitoring in production environments. It bridges the gap between data science and operations teams, addressing specific ML model management challenges. MLOps provides tools for monitoring and observing model performance, increasing collaboration and enabling continuous processes of development, testing, and operational monitoring. The practice encompasses various components such as data management, model training, deployment, serving, monitoring and logging, CI/CD, infrastructure management, and collaboration tools. MLOps aims to deliver faster development cycles, reliable systems, improved collaboration, accurate models in production, automated workflows, reproducible workflows, and lower operational costs. It differs from DevOps but shares similar goals, such as rapid innovation, scalable systems, and reliable performance. MLOps is essential for companies with machine learning models, unlocking revenue sources, saving time, and cutting costs through efficient workflows. The practice involves best practices like defining project scope, choosing ML tools wisely, automating testing, striving for continuous integration and delivery, applying data version control, implementing robust security measures, and continuous monitoring and logging. New Relic supports MLOps with features such as ML model performance monitoring, observability, and integrations with various MLOps tools, enabling teams to incorporate MLOps best practices into their workflow.