What is a potential challenge when deploying machine learning models?

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Overfitting the model
Data drift
Lack of training data
All of the above

Questions & Step-by-step Solutions

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Q: What is a potential challenge when deploying machine learning models?

Solution: Data drift, which occurs when the statistical properties of the input data change over time, is a significant challenge in model deployment.

Steps: 5

Step 1: Understand what machine learning models are. They are algorithms that learn from data to make predictions or decisions.
Step 2: Recognize that these models rely on data to function correctly.
Step 3: Identify that data drift is a situation where the data used by the model changes over time.
Step 4: Realize that when data drift happens, the model may not perform well because it was trained on different data.
Step 5: Conclude that monitoring and updating the model is necessary to handle data drift effectively.

What is a potential challenge when deploying machine learning models?

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