Exam Professional Machine Learning Engineer topic 1 question 251 discussion

Option B: While Vertex ML Metadata provides artifact lineage, it's less comprehensive for model comparison than Experiments. Options C and D: BigQuery ML is powerful for in-database model training, but it has limitations in pipeline orchestration, complex feature engineering, and detailed model comparison features, making it less suitable for this scenario.

Can anyone give a good reason for the answers without using ChatGPT or Gemini?

BQ ML falls a bit short when it comes to building pipelines that include feature engineering and experiment comparison (it's better to use Vertex Pipelines and do the comparisons using Vertex Experiments).

Flexibility and Control: Vertex AI Pipelines allow you to define a custom pipeline with separate components for data splitting, feature engineering, and XGBoost training using your preferred libraries (like BigQueryClient and xgboost). This provides more control and customization compared to BigQuery ML's limited model types and functionality. Feature Engineering and Data Splitting: Separate components enable clear separation of concerns and potentially parallel execution for efficiency. Autologging and Model Comparison: Vertex AI autologging simplifies capturing evaluation metrics during training. Vertex AI Experiments offer a centralized interface to compare metrics across different pipeline runs (potentially with varying hyperparameter configurations).

see b1a8fae

A: Leverage Vertex AI Pipelines and Experiments

My Answer: A A: CORRECT: It involves proper data splitting into training and evaluation sets and conducting feature engineering within the pipeline, fulfilling steps 1 and 2. Enabling autologging of metrics ensures that you can track and compare the performance of different model executions, fulfilling step 3. B: Not Correct: Better use Vertex AI Experiments C and D: Not Correct: BigQuery ML lacks functionalities for comparing models across pipeline runs. You would need to rely on external tools or custom scripts to extract and compare evaluation metrics, making the process less streamlined.

Compare models in different pipeline executions -> go for Vertex AI experiments