Topic 1 Question 55
You recently joined a machine learning team that will soon release a new project. As a lead on the project, you are asked to determine the production readiness of the ML components. The team has already tested features and data, model development, and infrastructure. Which additional readiness check should you recommend to the team?
Ensure that training is reproducible.
Ensure that all hyperparameters are tuned.
Ensure that model performance is monitored.
Ensure that feature expectations are captured in the schema.
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I think it should be C
👍 19inder00072021/07/06A - important one before moving to the production
👍 9ralf_cc2021/07/09- 正解だと思う選択肢: C
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 .
👍 4John_Pongthorn2023/02/15
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