MLOps for Health care NLP Services

Natural language processing (NLP) models are widely utilized in health care for many different use cases, from reducing operational cost to improving the quality of health care and making better predictions and diagnoses. With the recent advances in artificial intelligence, machine learning and deep learning models have become prevalent in NLP. Utilizing state of the art technology and ability to use abundance of data to train models greatly increases the accuracy of those models in many use cases. But in order to use those models in production environment, the processes of training data collection, model training, finetuning, monitoring and production deployment need to be operationalized, i.e. the traditional DevOps practices have to be extended to operationalize the models. This presentation will introduce a few health care NLP use cases, and the reference architecture and implementation of Dev/Model Ops for those NLP services.