Responsive parallelized architecture for deploying deep learning models in production environments
This addresses the practical need for fast CV processing in recruitment systems, though it appears incremental by optimizing deployment of existing deep learning methods.
The paper tackles the problem of efficiently parsing unstructured CV documents for candidate shortlisting by proposing a responsive parallelized architecture that uses hierarchically-refined label attention networks for named entity recognition, achieving parsing times of less than 700 milliseconds per CV in sequential request flows.
Recruiters can easily shortlist candidates for jobs via viewing their curriculum vitae (CV) document. Unstructured document CV beholds candidate's portfolio and named entities listing details. The main aim of this study is to design and propose a web oriented, highly responsive, computational pipeline that systematically predicts CV entities using hierarchically-refined label attention networks. Deep learning models specialized for named entity recognition were trained on large dataset to predict relevant fields. The article suggests an optimal strategy to use a number of deep learning models in parallel and predict in real time. We demonstrate selection of light weight micro web framework using Analytical Hierarchy Processing algorithm and focus on an approach useful to deploy large deep learning model-based pipelines in production ready environments using microservices. Deployed models and architecture proposed helped in parsing normal CV in less than 700 milliseconds for sequential flow of requests.