The Mars On Ray scientific computing framework combines XGBoost on Ray for end-to-end AI pipeline development. Mars is a unified tensor-based framework that scales NumPy, pandas, and scikit-learn functions, while Ray provides a simple, universal API for building distributed applications. The Mars On Ray architecture allows for seamless integration with Ray's large machine learning ecosystem, enabling fast and adaptive scale-out and scale-in, as well as automatic recovery from worker failures. XGBoost on Ray supports multi-node and multi-GPU parallel training, seamless integration with the popular distributed hyper-parameter tuning library Ray Tune, advanced fault tolerance mechanisms, loading distributed DataFrames as input data, and efficient data exchange through the shared memory object store. Mars On Ray is widely used at Ant Group and in the open source Mars Community, and its development continues to optimize performance, scalability, and efficiency.