AI Snips
Chapters
Transcript
Episode notes
Shift Focus From Models To Data
- Data-centric AI focuses on systematically engineering and improving the data used to build AI systems.
- This shifts attention from inventing new models to refining data quality and consistency to boost performance.
Make Data Engineering Systematic
- Build tools and checklists to make data engineering repeatable instead of relying on individual skill or luck.
- Invest in software and processes that let many teams systematically clean and validate datasets.
Iterate Synthetic Data Generation
- Use synthetic data and data augmentation iteratively: train models, identify gaps, then generate targeted synthetic examples and retrain.
- Close the loop between model training and data generation to produce the exact data you need.