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Question 31/123
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?
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?
Correct Answer: C
Scheduling a weekly query in BigQuery to compute the success metric is a cost-effective way to monitor the model's performance. BigQuery allows you to run complex queries on large datasets in a cost-effective and performant manner. By using BigQuery, you can compute the success metric on a regular basis without incurring the additional costs of other services such as Vertex AI or Cloud Composer.
Additionally, by scheduling the query to run weekly, you can ensure that you are monitoring the model's performance in a timely manner, while still providing enough time for the model to degrade below the acceptable baseline. You can then use the results of the query to determine when retraining is necessary.
Additionally, by scheduling the query to run weekly, you can ensure that you are monitoring the model's performance in a timely manner, while still providing enough time for the model to degrade below the acceptable baseline. You can then use the results of the query to determine when retraining is necessary.
- Question List (123q)
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