Notes on training data, labeling workflows, quality control, eval design, model feedback loops, and AI data infrastructure.
Topics
- Evaluation design and methodology
- Labeling workflows and quality assurance
- Data pipelines for ML training and fine-tuning
- Model feedback loops and iterative improvement
- Infrastructure for data-centric AI
Notes
Research notes and essays coming soon.