Netflix has made a case that its in-house content recommendation engine has benefited from the consolidation of machine learning (ML) models – eliminating redundancy and improving maintainability of systems. The feat may or may not be related to Netflix’s recent shuttering of the flawed percentage-based match score it employed for content recommendations back in 2017. Whether or not the streamer’s ML consolidation has had a noticeable impact on end users, in terms of the potency of recommended titles generating click-throughs and full views, is a question which has not been answered by the latest post from the official Netflix Tech Blog. First and foremost, consolidation of ML models reduces the number of lines of code, and in turn will reduce…