Resume parsing is a tool that analyzes a CV or a resume document and converts it into structured information for reporting, storing, analyzing, and screening. For a long time now, resumes have been screened and shortlisted manually. Recruiters would have to look through each resume separately and screen them based on skills, experience, education, etc. This process of shortlisting candidates took an immense amount of time and increased the cost of hire. This method also makes the company lose quality candidates as recruiters usually go through thousands of resumes and then shortlist until one is selected.

As a business buzzword, "machine learning" doesn't measure up to some of its more popular cousins – big data and transformation being two of the standouts – but this may not be the case for much longer. What was once something only used by people working in fields like software engineering, statistical modeling, and artificial intelligence, is now being used in many more recognizable and consumable forms. Online services such as Google, Netflix, Amazon, and Spotify all use machine learning to improve the customer experience. For example, if you've been streaming Barry White all week, Spotify's "Discover Weekly" playlist will begin suggesting other similar artists. Listeners may start seeing Luther Vandross pop up in their playlist, for example.