How to make a wise machine learning platforms comparison

Organizations should first consider the data types and data management features the product or service offers and whether they are available for a customer's on-premises systems, in a private or public cloud, or in a hybrid IT environment. For instance, does the platform support big data initiatives and allow users to build machine learning models with data gathered from a variety of sources, including text, images, multimedia and location systems? Data preparation features should also factor into a buying decision and machine learning platforms comparison, including data aggregation, sorting, filtering and integration. Does the platform handle data preparation faster by identifying common quality problems, for example? Also important are the visualization and exploratory tools the product or service offers.