Big data and ML: Interaction & Challenges

A broad variety of applications are making use of machine learning (ML). In recent years, it's been pushed to the forefront because of the advertising of big data. In the face of massive data, ml algorithms haven't been this promising, and this is due to massive breaches in databases. However, although big data allows ML algorithms to identify finer patterns as well as generate more precise and timely recommendations than ever before, it poses significant obstacles to ML, such as model adaptability and edge systems. A framework for ML on large data (MLBiD) is presented in this study to lead the consideration of its advantages and disadvantages. Pre-processing, learning, and assessment are all part of the ML framework. Big data, users, domains, and systems are all included in the framework as well. Issues and challenges related to protecting database, gathered reports, program resources, which are faced by the system has been pointed out. Objective lies in presenting solutions for the same using ML.