# Engineering Reliable Deep Learning Systems

arXiv: https://arxiv.org/abs/1910.12582 · HTML: https://mlsanity.com/paper/1910.12582/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2019.

## Built on by (most cited first, 6 of 6)

- [Software Engineering for AI-Based Systems: A Survey](https://mlsanity.com/paper/2105.01984/) (2021)
- [The Pipeline for the Continuous Development of Artificial Intelligence Models -- Current State of Research and Practice](https://mlsanity.com/paper/2301.09001/) (2023)
- [Quality Assurance Challenges for Machine Learning Software Applications During Software Development Life Cycle Phases](https://mlsanity.com/paper/2105.01195/) (2021)
- [MMRNet: Improving Reliability for Multimodal Object Detection and Segmentation for Bin Picking via Multimodal Redundancy](https://mlsanity.com/paper/2210.10842/) (2022)
- [Data Requirement Goal Modeling for Machine Learning Systems](https://mlsanity.com/paper/2504.07664/) (2025)
- [Software Engineering for AI-driven Building Operation](https://mlsanity.com/paper/2608.16237/) (2026)

## Abstract (arXiv)

Recent progress in artificial intelligence (AI) using deep learning techniques has triggered its wide-scale use across a broad range of applications. These systems can already perform tasks such as natural language processing of voice and text, visual recognition, question-answering, recommendations and decision support. However, at the current level of maturity, the use of an AI component in mission-critical or safety-critical applications can have unexpected consequences. Consequently, serious concerns about reliability, repeatability, trust, and maintainability of AI applications remain. As AI becomes pervasive despite its shortcomings, more systematic ways of approaching AI software development and certification are needed. These fundamental aspects establish the need for a discipline on "AI Engineering". This paper presents the current perspective of relevant AI engineering concepts and some key challenges that need to be overcome to make significant progress in this important area.