Exam Professional Machine Learning Engineer topic 1 question 242 discussion

Why B? Centralized Tracking: Vertex AI Experiments provides a central location to track and compare models trained in both pipelines and notebooks. Reduced Overhead: Submitting pipelines as experiment runs leverages the existing pipeline infrastructure for logging and avoids creating additional pipeline steps for all models. Notebook Integration: Vertex AI SDK allows notebooks to log parameters and metrics directly to the experiment, simplifying data collection from notebooks. why not C? C. All Models in Pipelines: Moving all models to pipelines might not be feasible or desirable. Pipelines are best suited for automated, repeatable training, while notebooks offer flexibility for exploration.

B. Create a Vertex AI experiment. Submit all the pipelines as experiment runs. For models trained on notebooks log parameters and metrics by using the Vertex AI SDK.

My Answer: B A: Not Correct: Not the best approach compared with Vertex AI experiment that does the same B: CORRECT: By submitting all pipelines as experiment runs, you can centralize the storage of parameters and metrics for models trained in Vertex AI Pipelines. This approach minimizes effort by providing a unified platform for storing and comparing model performance across different services. C: Not Correct: not feasible or ideal for models trained on Vertex AI Workbench notebook instances. D: Not Correct: If only basic parameter and metric storage is needed, and your team prioritizes simplicity over in-depth comparison, option D could be an alternative. For more complex scenarios requiring comprehensive analysis and comparison across diverse models, option B with Vertex AI Experiments

Divided between B and C. But logging parameters of models sounds easier than re-implementing a large amount of models as Vertex AI pipelines.

B is The correct answer here I believe - Vertex AI experiments - provides a unified way to store and compare model runs. pipeline runs - It provides a unified way to store and compare model runs. notebook instances - models trained on Vertex AI Workbench notebook instances, logging parameters and metrics using the Vertex AI SDK provides a consistent way to record the necessary information.

Options A and B: Logging metrics to BigQuery involves additional setup and integration efforts. Option D: Loading Vertex ML Metadata into a pandas DataFrame for visualization requires manual work and doesn't leverage built-in visualization tools.