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Software engineering challenges of deep learning

Our research team share key software engineering challenges of deep learning - from development, to production and organisational challenges.

There are many challenges with building production-ready systems with deep learning components, especially if the company does not have a large research group and a highly developed supporting infrastructure.

The research paper “Software Engineering Challenges of Deep Learning,” written by Peltarion members Anders Arpteg, Björn Brinne, Luka Crnkovic-Friis and Jan Bosch, identifies and outlines the main software engineering challenges associated with building systems with deep learning components. Seven projects are described to exemplify the potential for making use of machine learning and specifically deep learning technology. Additionally, to clarify the problematic areas in more detail, a set of 12 challenges are identified and described in the areas of development, production and organizational challenges.

Why read this

The main focus of the paper is not to provide solutions, but rather to outline problem areas and, in that way, help guide future research. In addition, the outlined challenges also provide guidance on potential problem areas for companies interested in building high-quality deep learning systems.

The paper was presented at the 44th Euromicro Conference on Software Engineering and Advanced Applications in September 2018.

Read, print and download the paper here

  • Anders Arpteg

    Anders Arpteg

    Head of Research

    Anders Arpteg has been working with AI for 20 years in both academia and industry and holds a Ph.D. in AI from Linköping University. Previously, he headed up a research group at Spotify and is now heading up the research team at Peltarion. Anders is a member of the AI Innovation of Sweden steering committee, an AI adviser for the Swedish government, a member of the Swedish AI Agenda, Chairman of the Machine Learning Stockholm meetup group and a member of several other advisory boards. 

  • Björn Brinne

    Björn Brinne

    Head of Data Science

    Björn Brinne has over a decade of experience working in data science at companies such as Truecaller, King and Electronic Arts before joining Peltarion. He holds a Ph.D. in theoretical physics from Stockholm University and has contributed to many research papers across a range of academic fields, including computer science, string theory and computational biology.

  • Luka Crnkovic-Friis

    Luka Crnkovic-Friis

    CEO & Co-Founder

    Luka Crnkovic-Friis is the CEO of Peltarion and has more than 15 years of experience with neural networks and their industrial applications. He holds a master’s and a bachelor’s degree of science in electrical engineering from the KTH Royal Institute of Technology in Stockholm. After being awarded his master’s in 2004, Luka co-founded Peltarion with Måns Erlandson. They were convinced then – as they are now – that artificial intelligence represents the next Industrial Revolution, but that it needs the right tools in order to be truly accessible, affordable and usable for everyone.

  • Jan Bosch

    Jan Bosch

    Member of the Board

    Jan Bosch is a Professor of Software Engineering at Chalmers University of Technology in Gothenburg and has been a member of the board at Peltarion since September 2017. He also serves as the director of the Software Center, runs the consulting firm Boschonian AB and is the author of several books, as well as the editor for the Journal of Systems and Software and Science of Computer Programming.