Self-Learning AI Pipelines for Dynamic Big Data Feature Engineering

The growing volume, speed, and diversity of contemporary data streams require adaptive feature engineering pipelines that are able to learn adaptively on dynamic settings. To minimize the time spent in feature extraction, the proposed paper offers a Self-Learning AI Pipeline of Dynamic Big Data Feature Engineering, where all three-reinforcement learning, meta-learning, and automated data preprocessing mechanisms are integrated to optimize feature extraction in real time. This framework keeps track of the incoming data distributions, identifies concept drifts, and adjusts transformation strategies with feedback-based reinforcement agent. By using a hybrid design of a stream-based data ingestion, autoencoder-driven feature selection, hyperparameter tuning by Bayesian optimization, the adaptability is ensured and manual intervention is kept to a minimum. Large-scale benchmark datasets show that experiments with these models achieve high model accuracy, feature relevance and lower computational costs than traditional static pipelines. The proposed system accelerates predictive tasks of downstream the adaptation to a data drift by up to 30 percent, and the performance in the system increases by 1520 percent. This study forms a basis to autonomous and self-optimizing ecosystems of feature engineering in big data analytics, enabling scalable and intelligent data pipelines to support next-generation AI applications.