SE4ML - Software Engineering for Machine Learning - Nadia Nahar DataTalks.Club

We talked about:


Nadia’s background
Academic research in software engineering
Design patterns
Software engineering for ML systems
Problems that people in industry have with software engineering and ML
Communication issues and setting requirements
Artifact research in open source products
Product vs model
Nadia’s open source product dataset
Failure points in machine learning projects
Finding solutions to issues using Nadia’s dataset and experience
The problem of siloing data scientists and other structure issues
The importance of documentation and checklists
Responsible AI
How data scientists and software engineers can work in an Agile way



Links:


Model Card: https://arxiv.org/abs/1810.03993
Datasheets: https://arxiv.org/abs/1803.09010
Factsheets: https://arxiv.org/abs/1808.07261
Research Paper: https://www.cs.cmu.edu/~ckaestne/pdf/icse22_seai.pdf
Arxiv version: https://arxiv.org/pdf/2110.



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