Machine Learning for Big Data

Scalable machine learning models are applied to Big Data using frameworks such as TensorFlow, PyTorch, and MLlib (Apache Spark). Feature engineering, model training, and hyperparameter tuning require distributed computing environments. Techniques like federated learning allow training on decentralized data without transferring sensitive information.