Unification of Machine Learning Features

In the Information Age, Machine learning (ML) provides a competitive advantage to any business. Machine learning applications are not limited to driverless cars or online recommendations but are widely used in healthcare, social services, government systems, telecommunications, and so on. As many enterprises are trying to step up machine learning applications, it is critical to have a long-term strategy. Most of the enterprises are not able to truly realize the fruits of ML capabilities due to its complexity. It is easier to access a variety of data today due to data democratization, distributed storage, technological advancements, and big data applications. Despite easier data access and recent advancements in ML, developers still spend most of the time in data cleansing, data preparation, and data modeling for ML applications. These steps are often repeated and result in identical features. As identical features can have inconsistent processing while testing and training, more issues pop up at later stages in ML application development. The unification of ML features is an effective way to address these issues. This paper presents details about numerous methods to achieve ML features unification.