XGBoost is a widely used gradient-boosting framework that excels in predictive modeling due to its support for GPU training, distributed computing, and parallelization, making it efficient for both classification and regression problems. It offers excellent documentation and ease of use, making it a preferred choice for machine learning tasks across languages like R, Python, and C++. The article discusses the architecture of XGBoost, highlighting its parallelization, regularization, non-linearity, cross-validation, and scalability features, which contribute to its effectiveness in handling large datasets and non-linear data patterns. XGBoost's integration with Neptune enables automatic tracking of training details and metadata, facilitating experiment management and performance monitoring. Moreover, the article explains ensemble learning, particularly focusing on homogeneous and heterogeneous ensemble methods, and details techniques like bagging and boosting, which enhance the accuracy and performance of predictive models. Hyperparameter tuning for XGBoost is crucial for optimal performance, with Grid Search being a recommended approach to determine the best parameters. Despite its advantages, XGBoost has limitations, such as sensitivity to outliers and challenges with sparse data, but it remains a powerful tool for structured datasets, especially when combined with tools like Neptune for tracking and managing experiments.