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SE4ML - Software Engineering for Machine Learning - Nadia Nahar
MP3•Laman utama episod
Manage episode 358898792 series 2831626
Kandungan disediakan oleh DataTalks.Club. Semua kandungan podcast termasuk episod, grafik dan perihalan podcast dimuat naik dan disediakan terus oleh DataTalks.Club atau rakan kongsi platform podcast mereka. Jika anda percaya seseorang menggunakan karya berhak cipta anda tanpa kebenaran anda, anda boleh mengikuti proses yang digariskan di sini https://ms.player.fm/legal.
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
Join DataTalks.Club: https://datatalks.club/slack.html
Our events: https://datatalks.club/events.html
167 episod
MP3•Laman utama episod
Manage episode 358898792 series 2831626
Kandungan disediakan oleh DataTalks.Club. Semua kandungan podcast termasuk episod, grafik dan perihalan podcast dimuat naik dan disediakan terus oleh DataTalks.Club atau rakan kongsi platform podcast mereka. Jika anda percaya seseorang menggunakan karya berhak cipta anda tanpa kebenaran anda, anda boleh mengikuti proses yang digariskan di sini https://ms.player.fm/legal.
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
Join DataTalks.Club: https://datatalks.club/slack.html
Our events: https://datatalks.club/events.html
167 episod
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