In this episode of Safe and Sound AI, we dive into the challenge of drift in machine learning models. We break down the key differences between concept and data drift (including feature and label drift), explaining how each affects ML model performance over time. Learn practical detection methods using statistical tools, discover how to identify root causes, and explore strategies for maintaining model accuracy.
Read the article by Fiddler AI and explore additional resources on how AI Observability can help build trust into LLMs and ML models.

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