Model drift happens when an AI model’s performance degrades over time because the data it sees in the real world differs from the data it was trained on.
Why it matters:
- Leads to less accurate predictions and potentially harmful decisions.
- Common in environments that change quickly (e.g., finance, cybersecurity, supply chain).
- Can create hidden risks if not monitored.
Pro tip: Regularly retrain models with fresh data to keep them relevant and accurate.
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