Page Reader AI | Exam Professional Machine Learning Engineer topic 1 question 147 discussion

Exam Professional Machine Learning Engineer topic 1 question 147 discussion - ExamTopics

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Problem: Efficient Experiment Tracking

A data science team requires a system to efficiently track various machine learning experiments, including feature changes, model architectures, hyperparameters, and accuracy metrics, while minimizing manual effort. They need an API for querying these metrics.

Solutions and Evaluation

The question presents four options, each using different Google Cloud Platform (GCP) services:

A. Vertex AI Pipelines and MetadataStore: Executes experiments using pipelines and stores results in MetadataStore, queried via the Vertex AI API.
B. Vertex AI Training and BigQuery: Executes experiments using training, writes metrics to BigQuery, and queries using the BigQuery API.
C. Vertex AI Training and Cloud Monitoring: Executes experiments using training, writes metrics to Cloud Monitoring, and queries using the Monitoring API.
D. Vertex AI Workbench and Google Sheets: Executes experiments in notebooks, collects results in Google Sheets, and queries using the Google Sheets API.

Option A is identified as the suggested answer due to its seamless integration and efficiency in managing machine learning experiments.

Suggested Answer

The best solution is to use Vertex AI Pipelines to manage and execute experiments and leverage Vertex AI's MetadataStore for efficient result tracking and querying via its API. This approach provides a cohesive and streamlined solution within the GCP ecosystem.

Your data science team needs to rapidly experiment with various features, model architectures, and hyperparameters. They need to track the accuracy metrics for various experiments and use an API to query the metrics over time. What should they use to track and report their experiments while minimizing manual effort?

A. Use Vertex Al Pipelines to execute the experiments. Query the results stored in MetadataStore using the Vertex Al API.
B. Use Vertex Al Training to execute the experiments. Write the accuracy metrics to BigQuery, and query the results using the BigQuery API.
C. Use Vertex Al Training to execute the experiments. Write the accuracy metrics to Cloud Monitoring, and query the results using the Monitoring API.
D. Use Vertex Al Workbench user-managed notebooks to execute the experiments. Collect the results in a shared Google Sheets file, and query the results using the Google Sheets API.

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