You are developing an ML model in a Vertex AI Workbench notebook. You want to track artifacts and compare models during experimentation using different approaches. You need to rapidly and easily transition successful experiments to production as you iterate on your model implementation. What should you do?
To rapidly and easily transition successful experiments to production while tracking artifacts and comparing models, you should initialize the Vertex SDK with the experiment name, log parameters, and metrics for each experiment, and attach dataset and model artifacts as inputs and outputs to each execution. After a successful experiment, creating a Vertex AI pipeline aids in transitioning experiments to production, thereby maintaining the balance between flexibility during experimentation and structured pipeline creation for production deployment.
Option B: Manually saving datasets and models to Cloud Storage and Model Registry introduces extra steps and potential for inconsistencies. Options C and D: Prioritizing pipeline creation limits flexibility and visibility during the experimentation phase, making it harder to track artifacts and compare models effectively.
I agree with these comments >> I will go for A, because the requirement is "rapidly and easily" >> B: Manually saving datasets and models to Cloud Storage and Model Registry introduces extra steps and potential for inconsistencies. >> Options C and D: Prioritizing pipeline creation limits flexibility and visibility during the experimentation phase, making it harder to track artifacts and compare models effectively.
I will go for A, because the requirement is "rapidly and easily" transition successful experiments to production. Option B,C,D are too complex to conduct.
I believe is A for the same reasons that pikachu.
Answer B leverages more tools for responsability splitting : they are still tools for early experiments, but would help in the pipeline creation. C & D are overkill
Option A correctly describes how to rapidly and easily transition successful experiments to production by initializing the Vertex SDK with the experiment name, logging parameters and metrics, and attaching dataset and model artifacts. The second step of creating a Vertex AI pipeline after a successful experiment allows for easy iteration on the model implementation while maintaining track of the experiment's performance.