Machine Learning and Big Data

ML and Big Data are the blue-chips of the current IT Industry. Big data stores, analyzes and extracts information out of bulk data sets. On the other hand, Machine learning is the ability to automatically learn and improve from experience without being explicitly programmed.

Big Data is more of extraction and analysis of information from huge volumes of data. Machine Learning is more of using input data and algorithms for estimating unknown future results. Big data analysis is the unique way of handling bigger and unstructured data sets using tools like Apache Hadoop, MongoDB.

Big data is the type of data that may be supplied into the analytical system so that a ML model could ‘learn’ or in other words, improve the accuracy of its predictions. Big data from sensors are used to train, test and retrain a ML model.

 With voluminous unstructured and structured data pouring in incessantly, it becomes difficult to extract insight from it and the situation can quickly spiral out of control. With massive computational power, ML systems help companies manage, analyze, and use their data far more successfully than ever before. Machine Learning usually works with huge chunks of data and this is where Big Data comes into picture. Because predictive analytics can mine through large quantities of data quickly and efficiently, many companies are utilizing big data to understand their consumers’ spending habits. Data findings from predictive analytics provide timely information about consumers that can lead to increased engagement and improved business planning.