This new integration between Ray and MLflow enables developers to build, train, and deploy machine learning models with increased efficiency. By combining Ray's distributed libraries for scaling training and serving with MLflow's end-to-end model lifecycle management, these integrations simplify the process of building and deploying ML models, allowing engineers to focus on developing and improving their algorithms rather than managing the scaling and deployment burden. The integration includes features such as automatic logging of hyperparameters and results using the MLflow Tracking API, convenient loading of models as Python functions with Ray Serve, and a seamless workflow for deploying ML models at scale. This new integration makes it much easier to build distributed ML applications and take them to production, saving developers time and effort in the process.