Exam Professional Machine Learning Engineer topic 1 question 55 discussion

I think it should be C

A - important one before moving to the production

C) Model monitoring

C is not a readiness check. Monitoring is a continuous effort. IMO A is the correct answer. If the training is not reproducible it's not ready for production. If any error happens, data drifts / skews, then there is no way to recreate the model. This is a check BEFORE going to production. Once it's in production, then yes C is important.

Monitoring is crucial. So - C

Went with C

I'll go with C. Monitoring model performance is an important aspect of production readiness. It allows the team to detect and respond to changes in performance that may affect the quality of the model. The other options are also important, but they are more focused on the development phase of the project rather than the production phase.

Hey! all guys A+B+D=The team has already tested features and data, model development, and infrastructure. we are about to go live with production. Monitoring readiness is the last thing to account for. It will be very rediculous if you launch model as production regardless of how we will have about monitoring. you will lauch model as production for while and will make plan to model performance monitoring later ??? you are too reckless. Pls . Read it carefully https://developers.google.com/machine-learning/testing-debugging/pipeline/production https://developers.google.com/machine-learning/testing-debugging/pipeline/overview#what-is-an-ml-pipeline. You Most guys prefer A : https://developers.google.com/machine-learning/testing-debugging/pipeline/deploying I think that it is all about model development prior to deploying .

I think that your team ensure that all hypermarameters were turned yet when tested features... i think that it's more important that they ensure that model performance is monitored than thaining is reproducible for best practices. https://cloud.google.com/architecture/ml-on-gcp-best-practices

Reproducible Training is more likely to be in the Deployment step in that it referred to the question "The team has already tested features and data, model development" but the question focuses on Production readiness https://developers.google.com/machine-learning/testing-debugging/pipeline/production Monitor section is part of this above link

C, for me.

It's mentioned that the team has already tested features and data, implying that data generation is reproducible. If you have to test features data has to be reproducible to compare model outputs. ( https://developers.google.com/machine-learning/data-prep/construct/sampling-splitting/randomization). Hence C makes more sense

https://cloud.google.com/ai-platform/docs/ml-solutions-overview

With the specific focus on "production readiness" as stated, I'd pick C above the others.

I think it's C. A is related to infrastructure, B is related to model development and D is related to Data and features. It clearly mentioned that team has already tested for model development, data and features and infrastructure.

"production readiness" means that we are still in dev-test phase , and "performance monitoring" happens in production , and what if monitoring is applied but the model re-train is difficult , so "A" is the best answer

A makes more sense than C.