What is AI model drift and why is it important to address?
AI model drift refers to the degradation of a model's performance over time due to changes in the underlying data patterns. It's crucial to address it to maintain accuracy and reliability.
What are some common causes of model drift?
Common causes include changes in data distribution, evolving user behavior, and external factors like market trends or seasonality.
How can I detect model drift in my AI systems?
You can detect model drift using techniques like monitoring performance metrics, statistical tests, and visualizations of data distributions.
What strategies can I implement to mitigate model drift?
Strategies include retraining models regularly, using adaptive learning techniques, and implementing feedback loops to adjust to new data.
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