In this talk, I will go over the common machine learning system design principles in speech models.Firstly we will discuss, when Natural Language Processing (NLP) models have to be stacked after speech recognition models, the common error recovery mechanisms followed for a large scale prod system.If the model has to tackle growing use-cases like learning new key words, how the core model has to designed to accommodate new use-cases without retraining the entire DNN model. In this talk, the audience will learn that in-practice speech models and NLP compliment each other for better recognition rate.