Continual learning series: keeping training data clean when feedback, noise and bias creep in
goyalshaliniuk · x · 2026-11-13
Part 3 of a thread on building continuously learning AI systems, focused on data quality.
Key failure modes:
- Feedback can be wrong or misleading
- Incoming data may contain duplicates and noise
- Biased samples distort model behavior
- Auto-collected data can carry unexpected patterns
Recommended mitigations: build data validation pipelines, filter unreliable feedback, track data provenance, and run quality checks before every model update.
Related event: The Seven Engineering Challenges of Continuously Learning AI Systems(5 posts)→
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