Continuous Deep Learning: A Workflow to Bring Models into Production

TL;DR

This paper defines a comprehensive deep learning workflow that integrates hardware, data management, and model optimization, bridging the gap between classical ML processes and deep learning development.

Contribution

It introduces a detailed end-to-end deep learning workflow and demonstrates its practical implementation through a prototype system with real use cases.

Findings

Feasibility of the proposed workflow demonstrated

Prototype system successfully applied to two use cases

Workflow addresses deep learning-specific challenges

Abstract

Researchers have been highly active to investigate the classical machine learning workflow and integrate best practices from the software engineering lifecycle. However, deep learning exhibits deviations that are not yet covered in this conceptual development process. This includes the requirement of dedicated hardware, dispensable feature engineering, extensive hyperparameter optimization, large-scale data management, and model compression to reduce size and inference latency. Individual problems of deep learning are under thorough examination, and numerous concepts and implementations have gained traction. Unfortunately, the complete end-to-end development process still remains unspecified. In this paper, we define a detailed deep learning workflow that incorporates the aforementioned characteristics on the baseline of the classical machine learning workflow. We further transferred the conceptual idea into practice by building a prototypic deep learning system using some of the latest technologies on the market. To examine the feasibility of the workflow, two use cases are applied to the prototype.