Machine Learning at Scale
Data by itself is a static, lifeless entity. You need analytics to breathe life into it and make it talk or even sing. The most sophisticated and popular class of such analytics revolves around nowcasting, forecasting, and recommendations, more generally known as machine learning and data mining. Machine-learning algorithms learn patterns in data and can then be used to make predictions, whereas data mining helps extract structure from unstructured data. Machine learning at scale is the key to practical predictions and recommendations, which are essential to drive the needs of consumers: commercial, academic, or scientific. This chapters uses MLlib to enable such applications.