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Data Drift - How do you handle data drift?


Handling data drift involves strategies such as constant monitoring and assessment of a model's performance, updating the model with fresh data, and employing machine learning models that exhibit a higher resistance to drift. More information on dealing with post-deployment data science issues like drift can be found in our DataFramed podcast episode.