In this talk we propose the DeepXML framework for deep extreme multi-label learning and apply it to short-text document classification. We demonstrate that DeepXML can: (a) be used to analyze seemingly disparate deep extreme classifiers; (b) can lead to improvements in leading algorithms such as XML-CNN & MACH when they are recast in the proposed framework; and (c) can lead to a novel algorithm called Astec which can be up to 12% more accurate and up to 40x faster to train than the state-of-the-art for short text document classification. Finally, we show that when flighted on Bing, Astec can be used for personalized search, ads and recommendation for billions of users. Astec can handle billions of events per day, can process more than a hundred thousand events per second and leads to a significant improvement in key metrics as compared to state-of-the-art methods in production.