Exam Professional Machine Learning Engineer topic 1 question 175 discussion

I go with B: “Custom prediction routines (CPR) lets you build custom containers with pre/post processing code easily, without dealing with the details of setting up an HTTP server or building a container from scratch.” (https://cloud.google.com/vertex-ai/docs/predictions/custom-prediction-routines). This alone makes B preferable to C and D, provided lack of complex model architecture. Regarding A, pre-built containers only allow serving predictions, but not preprocessing of data (https://cloud.google.com/vertex-ai/docs/predictions/pre-built-containers#use_a_prebuilt_container). B thus remains the most likely option.

B - Creating a custom container without CPR adds additional complexity. i.e. write model server write dockerfile and also build and upload image. Where as using a CPR only requires writing a predictor and using vertex SDK to build image. https://cloud.google.com/vertex-ai/docs/predictions/custom-prediction-routines

you want to minimize code... all other you need code...

https://cloud.google.com/vertex-ai/docs/predictions/custom-prediction-routines

agree with shadz10

My choose: C Option C ensures that the scikit-learn model is properly packaged, deployed, and integrated with Vertex AI services while minimizing the need for additional code beyond what is necessary for customizing the serving function. Option B is not considered correct because it suggests wrapping the scikit-learn model in a custom prediction routine (CPR), which might not be the most suitable approach for deploying scikit-learn models on Vertex AI. Options A and D using InstanceConfig, that is limited for preprocessing. Uploading the container without a serving function won't work.

Considering the goal of minimizing additional code and complexity, option D - "Create a custom container for your scikit-learn model, upload your model and custom container to Vertex AI Model Registry, deploy your model to Vertex AI Endpoints, and create a Vertex AI batch prediction job that uses the instanceConfig.instanceType setting to transform your input data" seems to be a more straightforward and efficient approach. It involves customizing the container for the scikit-learn model, leveraging the Vertex AI Model Registry, and utilizing the specified instance type for batch prediction without introducing unnecessary complexity like custom prediction routines.