One of our recurrent ML pipelines become constantly over-fitting in its daily training recently. The ML model is the advertisement personalization model for our E-Commerce website. What do you think could be the problem?
There could be multiple reasons why your model is overfitting. Some possible reasons are:

-Insufficient and biased data: The training data used to build the model may not be diverse enough to represent the entire population. It could be biased towards certain demographics or regions, leading to overfitting on the available data.

-Data leakage: The model may be trained on features that are not available during production, leading to overfitting. For instance, if your model is trained on features such as time and date, it may be overfitting on those features instead of learning the underlying patterns.

-Complex model architecture: Your model architecture may be too complex, with too many parameters, making it prone to overfitting. This can occur when the model is trying to memorize the training data instead of learning the underlying patterns.

-Inappropriate hyperparameters: The hyperparameters used for training the model may not be appropriate for the given data, leading to overfitting. For instance, a large learning rate may cause the model to converge too quickly and overfit the data.

-Lack of regularization: Regularization techniques such as L1, L2, and dropout can help prevent overfitting by reducing the model's complexity and encouraging generalization.

To address the overfitting problem, you could try the following:

-Collect more diverse and unbiased data.

-Check for data leakage by reviewing the features used in the model and ensuring that they are available during production.

-Simplify the model architecture or use techniques such as early stopping to prevent overfitting.

-Tune the hyperparameters to ensure that they are appropriate for the data.

-Apply regularization techniques such as L1, L2, and dropout to the model.

-Validate the model's performance using a holdout or validation dataset, and adjust the model as necessary.
That was a really detailed and insightful explanation and suggestion. I'll take necessary steps to overcome the overfitting issue.
Glad to hear that the explanation was helpful. Best of luck in addressing the overfitting issue in your ML pipeline!