Machine Learning's Sweet Spot: Pure Approaches in NLP and Document Analysis - KDnuggets

Machine Learning is slowly but surely leaving academic circles and enthusiasts' nighttime projects to relocate to business applications. While some solutions have quickly adopted modern ML-based algorithms (Deep Learning) successfully, image recognition for example, others have been struggling with achieving production-grade results, leading to stark statistics highlighting that 85% of ML projects in major corporations never see the light of day. This is a defining step for ML as a whole, since the expectations companies have when onboarding a new technology are high, there's a demand for higher quality than what one usually chases in a lab, and a positive return on investment is considered the norm. Nevertheless, experiments in this field are the new normal for actors in all industries. And when ML is applied to Document Analysis, some conclusions are fairly common.