Foundations Of Building Next-Gen Data Platforms For Machine Learning Tasks

Every machine learning lifecycle starts with a business problem. It can be about the increase in sales, make predictions, cutting costs or whatever it is that brings profits to the organisation. Once a business problem is classified under artificial intelligence or machine learning, the next question is -- what data will be used to solve it? Answering this question is followed by integrating pipelines where the data gets processed, exploratory data analysis is carried out and feature engineering is done. In order to meet the growing demands of the customers, companies which use data platforms have to handle the scale, agility, and flexibility to combine different types of data and analytics approaches which will allow them to transform data into a valuable corporate asset.