• Here are some common issues that can arise when using machine learning (ML):

Poor data quality

Noisy, incomplete, or inaccurate data can lead to inaccurate predictions, low-quality results, and faulty programming. You can try to evaluate and improve your data before you start using it by using data governance, integration, and exploration.

Inadequate training data

The quality of the data used to train ML algorithms is critical, and non-representative training data can affect the model’s ability to generalize.

Overfitting and underfitting

Overfitting occurs when a model is trained with too much data, which can negatively impact performance. Underfitting occurs when data doesn’t explicitly link input and output variables.

Delayed implementation

ML models can be efficient, but they can also be slow due to data overload, slow programs, and excessive requirements. ^