Exam Professional Machine Learning Engineer topic 1 question 210 discussion

Refactored Transformation Code: By refactoring the transformation code from the batch pipeline, you can create a reusable module that performs the same preprocessing steps. Same Code in Endpoint: Utilize the refactored code within your real-time inference endpoint. This ensures the data is preprocessed identically to how it was preprocessed during training.

agree with guilhermebutzke

My Answer B: B. This option ensures that the preprocessing logic used during training, which has already been validated and tested, is applied consistently during real-time inference. By making the transformation code reusable outside of the batch pipeline and utilizing it in the endpoint, you ensure that the same preprocessing steps are applied to incoming data during inference, thus maintaining consistency between training and serving. A:  While data validation is essential, it only ensures the format. It doesn't guarantee consistent preprocessing logic between training and serving. C: Sharing code with end-users might not be desirable for security or maintainability reasons. D: Batching introduces latency and might not be suitable for real-time needs. Additionally, using the entire Dataflow pipeline might be inefficient for individual requests.

The transformation logic code in the serving_fn function defines the serving interface of your SavedModel for online prediction. If you implement the same transformations that were used for preparing training data in the transformation logic code of the serving_fn function, it ensures that the same transformations are applied to new prediction data points when they're served. https://www.tensorflow.org/tfx/guide/tft_bestpractices

A. Data validation: While essential, it doesn't guarantee consistency if the preprocessing logic itself differs between pipeline and endpoint. C. Sharing code with end users: This shifts the preprocessing burden to end users, potentially leading to inconsistencies and errors, and isn't feasible for real-time inference. D. Batching real-time requests: This introduces latency and might not align with real-time requirements, as users expect immediate responses.