What are the challenges of deploying Data Science models in production?
The question is about data science
Answer:
Deploying Data Science models to production faces challenges such as model drift, where the performance of a model degrades because of a change in data patterns. Besides that, very important challenges are the scalability of the model, integration with existing systems, and keeping data secure. These issues require continuous monitoring and therefore updating of the models.
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