# Continuous Deep Learning: A Workflow to Bring Models into Production

arXiv: https://arxiv.org/abs/2208.12308 · HTML: https://mlsanity.com/paper/2208.12308/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2022.

## Built on by (most cited first, 1 of 1)

- [UCTB: An Urban Computing Tool Box for Building Spatiotemporal Prediction Services](https://mlsanity.com/paper/2306.04144/) (2023)

## Abstract (arXiv)

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.