The revenge of the data scientist
4 days ago
- #AI-evaluation
- #data-science
- #machine-learning
- The heyday of data scientists may be challenged as LLMs enable AI integration without them, shifting roles.
- Data science fundamentals like exploratory analysis, model evaluation, and experimental design remain critical, especially for evaluating AI systems.
- Common pitfalls in AI development include relying on generic metrics, unverified judges, poor experimental design, bad data/labels, and over-automation.
- Looking at data directly through traces and custom analysis is the highest ROI activity for diagnosing application failures.
- Data scientists emphasize application-specific metrics, domain-expert labeling, and skepticism towards data quality to ensure reliable AI systems.