What is a key advantage of preprocessing features with Apache Beam?

Preprocessing features with Apache Beam offers the key advantage that the same code can be used for both model training and evaluation. This is significant because consistency in data processing ensures that the model sees the same representation of the data during training and when making predictions. By using a unified approach to preprocess data, you can avoid discrepancies that might arise from different processing methods, which could lead to poor model performance and unreliable evaluation metrics.

The ability to apply the same preprocessing logic in both contexts helps streamline the machine learning pipeline, making it more efficient and easier to maintain. This approach also aids in version control, as any changes in preprocessing can be propagated across both training and evaluation phases without the risk of error.

While other options discuss a range of beneficial aspects like simplified coding for real-time predictions or optimization, they do not encapsulate the core advantage of ensure uniformity across model training and evaluation phases in the same manner as using Apache Beam for preprocessing.