Topic 1 Question 136

Professional Machine Learning Engineer

Topic 1 Question 136
You work for an online publisher that delivers news articles to over 50 million readers. You have built an AI model that recommends content for the company’s weekly newsletter. A recommendation is considered successful if the article is opened within two days of the newsletter’s published date and the user remains on the page for at least one minute.

All the information needed to compute the success metric is available in BigQuery and is updated hourly. The model is trained on eight weeks of data, on average its performance degrades below the acceptable baseline after five weeks, and training time is 12 hours. You want to ensure that the model’s performance is above the acceptable baseline while minimizing cost. How should you monitor the model to determine when retraining is necessary?
- Use Vertex AI Model Monitoring to detect skew of the input features with a sample rate of 100% and a monitoring frequency of two days.
- Schedule a cron job in Cloud Tasks to retrain the model every week before the newsletter is created.
- Schedule a weekly query in BigQuery to compute the success metric.
- Schedule a daily Dataflow job in Cloud Composer to compute the success metric.
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- 正解だと思う選択肢: C
  "All the information needed to compute the success metric is available in BigQuery" and "on average its performance degrades below the acceptable baseline after five weeks" so once per week is enough to check models performance. And it's the cheapest solution too.
  
  👍 3
  pshemol2022/12/21
- 正解だと思う選択肢: C
  Option C is the best answer. Since all the information needed to compute the success metric is available in BigQuery and is updated hourly, scheduling a weekly query in BigQuery to compute the success metric is the simplest and most cost-effective way to monitor the model's performance. By comparing the computed success metric against the acceptable baseline, you can determine when the model's performance has degraded below the threshold, and retrain the model accordingly. This approach avoids the cost of additional monitoring infrastructure and leverages existing data processing capabilities.
  
  👍 3
  TNT872023/03/07
- The anwner is on here https://cloud.google.com/blog/topics/developers-practitioners/continuous-model-evaluation-bigquery-ml-stored-procedures-and-cloud-scheduler
  
  👍 2
  John_Pongthorn2023/01/23
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