DataTalks.Club

SE4ML - Software Engineering for Machine Learning - Nadia Nahar

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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.


      • Free data engineering course: https://github.com/DataTalksClub/data-engineering-zoomcamp

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