Deploying machine learning models to production is challenging, partially due to the misalignment between software engineering and machine learning disciplines but also due to potential practitioner knowledge gaps. To reduce this gap and guide decision-making, we conducted a qualitative investigation into the technical challenges faced by practitioners based on studying the grey literature and applying the Straussian Grounded Theory research method. We modelled current practices in machine learning, resulting in a UML-based architectural design decision model based on current practitioner understanding of the domain and a subset of the decision space and identified seven architectural design decisions, various relations between them, twenty-six decision options and forty-four decision drivers in thirty-five sources. Our results intend to help bridge the gap between science and practice, increase understanding of how practitioners approach deployment of their solutions, and support practitioners in their decision-making.
Wed 17 MayDisplayed time zone: Hobart change
13:45 - 15:15 | Software architectures and designShowcase / Technical Track / SEET - Software Engineering Education and Training / NIER - New Ideas and Emerging Results at Meeting Room 102 Chair(s): Davide Taibi Tampere University | ||
13:45 15mTalk | Robustification of Behavioral Designs against Environmental Deviations Technical Track Changjian Zhang Carnegie Mellon University, Tarang Saluja Swarthmore College, Rômulo Meira-Góes Carnegie Mellon University, Matthew Bolton University of Virginia, David Garlan Carnegie Mellon University, Eunsuk Kang Carnegie Mellon University Pre-print | ||
14:00 15mTalk | A Qualitative Study on the Implementation Design Decisions of Developers Technical Track Jenny T. Liang Carnegie Mellon University, Maryam Arab George Mason University, Minhyuk Ko Virginia Tech, Amy Ko University of Washington, Thomas LaToza George Mason University Pre-print | ||
14:15 15mTalk | Designing for Real People: Teaching Agility through User-Centric Service Design SEET - Software Engineering Education and Training Robert Chatley Imperial College London, Tony Field Imperial College London, Mark Wheelhouse Imperial College London, Carolyn Runcie Royal College of Art, Nick de Leon Royal College of Art, Clive Grinyer Royal College of Art Pre-print | ||
14:30 15mTalk | A Decision Model for Choosing Patterns in Blockchain-Based Applications Showcase Xiwei (Sherry) Xu CSIRO’s Data61, H M N Dilum Bandara Data61, CSIRO, Qinghua Lu CSIRO’s Data61, Ingo Weber TU Munich & Fraunhofer, Munich, Len Bass Carnegie Mellon University, Liming Zhu CSIRO’s Data61 | ||
14:45 15mTalk | Architectural Design Decisions for Machine Learning Deployment Showcase | ||
15:00 7mTalk | Handling Communication via APIs for Microservices NIER - New Ideas and Emerging Results | ||
15:07 7mTalk | Open Design Case Study - A Crowdsourcing Effort to Curate Software Design Case Studies SEET - Software Engineering Education and Training Chun Yong Chong Monash University Malaysia, Eunsuk Kang Carnegie Mellon University, Mary Shaw Carnegie Mellon University Pre-print |