Papers › Continuous Deep Learning: A Workflow to Bring Models into Production
Continuous Deep Learning: A Workflow to Bring Models into Production
Janosch Baltensperger, Pasquale Salza, Harald C. Gall
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
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.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections