How Does Machine Learning Benefit from Scalable Architecture?
Machine learning (ML) algorithms are increasingly used in cancer research for tasks such as image recognition, predictive modeling, and drug discovery. Scalable architecture allows ML models to be trained on large datasets, improving their accuracy and effectiveness. Distributed computing frameworks like Apache Spark and TensorFlow enable the parallel processing of data, accelerating the training and deployment of ML models.
