Software Engineering Challenges of Deep Learning (1810.12034v1)

Abstract: Surprisingly promising results have been achieved by deep learning (DL) systems in recent years. Many of these achievements have been reached in academic settings, or by large technology companies with highly skilled research groups and advanced supporting infrastructure. For companies without large research groups or advanced infrastructure, building high-quality production-ready systems with DL components has proven challenging. There is a clear lack of well-functioning tools and best practices for building DL systems. It is the goal of this research to identify what the main challenges are, by applying an interpretive research approach in close collaboration with companies of varying size and type. A set of seven projects have been selected to describe the potential with this new technology and to identify associated main challenges. A set of 12 main challenges has been identified and categorized into the three areas of development, production, and organizational challenges. Furthermore, a mapping between the challenges and the projects is defined, together with selected motivating descriptions of how and why the challenges apply to specific projects. Compared to other areas such as software engineering or database technologies, it is clear that DL is still rather immature and in need of further work to facilitate development of high-quality systems. The challenges identified in this paper can be used to guide future research by the software engineering and DL communities. Together, we could enable a large number of companies to start taking advantage of the high potential of the DL technology.

• The paper identifies 12 key challenges in merging deep learning with production systems by categorizing issues into development, production, and organizational domains.
• Using insights from seven real-world projects, it reveals challenges like non-deterministic outputs, resource limitations, and dependency management struggles unique to DL.
• The findings stress the need for enhanced debugging, testing tools, and cross-disciplinary collaboration to overcome the gap between traditional SE practices and DL implementations.

Software Engineering Challenges of Deep Learning

The paper "Software Engineering Challenges of Deep Learning" by Anders Arpteg et al. conducts an in-depth exploration into the myriad challenges faced in integrating Deep Learning (DL) into production environments. The research makes significant strides in identifying and categorizing the primary hurdles in merging Software Engineering (SE) processes with DL systems. Researchers have traditionally acknowledged the power of DL in academic and large tech environments, but this paper highlights the gap in resources for smaller companies to adopt these technologies effectively.

Key Findings

The research is structured around seven diverse real-world ML projects, providing a comprehensive survey of SE challenges when implementing DL systems. These projects span various industries, including real estate valuation, oil recovery prediction, user retention, weather forecasting, credit card fraud detection, poker bot identification, and media recommendations.

Identified Challenges

The paper categorizes the challenges into three primary areas: development, production, and organizational, with a total of 12 challenges identified.

1. Development Challenges:
   • Non-deterministic outputs of DL models lead to difficulties in planning and estimating project timelines. New problems such as experiment management and resource limitations require attention. The lack of transparency and challenges in debugging DL models add complexity absent in traditional SE projects.
2. Production Challenges:
   • Dependency management stands as a major hurdle, compounded by the rapid evolution of hardware and software. Effective monitoring and logging of DL applications are crucial but underdeveloped areas. Unintended feedback loops present unique challenges, risking autonomous shifts in model behavior without explicit developer control.
3. Organizational Challenges:
   • Cultural differences in how teams within organizations approach DL projects can slow progress and uptake. Effort estimation is a non-trivial task in DL projects due to inherent uncertainties in model performance and project outcomes. Data privacy and safety also present formidable challenges in compliance with regulations like the GDPR.

Implications and Future Directions

The implications of these findings are significant for the future of DL adoption in industry. While DL holds immense potential to transform industries, realizing this requires addressing the outlined challenges. The paper does not propose solutions but rather provides a roadmap of challenges that need to be addressed by both the SE and DL communities to make DL accessible to a wider range of companies.

The findings suggest future work should aim at creating better tools and methodologies tailored to the unique aspects of DL systems. Improved practices for debugging, testing, and deploying DL models will be crucial. Additionally, fostering cross-disciplinary collaboration among data scientists, engineers, and business stakeholders will be key in overcoming the cultural and organizational barriers noted.

In conclusion, the integration of DL into practical, production-grade systems is shadowed by several SE challenges. As DL continues to mature, addressing these challenges is critical for leveraging its full potential across diverse industries. The paper by Arpteg et al. provides valuable insights that guide future work towards resolving these issues, empowering more companies to exploit the capabilities of DL systems efficiently and effectively.