How AI engineering differs from classical ML
The two ways of working stand at different points of the same problem. We will build the difference out of results you measured in this curriculum.
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Sources
- Sculley, D. et al. 2015 · Hidden Technical Debt in Machine Learning Systems · NeurIPS 2015
- Bommasani, R. et al. 2021 · On the Opportunities and Risks of Foundation Models · arXiv:2108.07258
- Shankar, S. et al. 2024 · Who Validates the Validators? Aligning LLM-Assisted Evaluation of LLM Outputs with Human Preferences · UIST 2024
- Paleyes, A. et al. 2022 · Challenges in Deploying Machine Learning: a Survey of Case Studies · ACM Computing Surveys 55(6)
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