NCA-GENL Question 180
Select 3You are managing a project where a large-scale Generative AI model is being fine-tuned. The model's performance shows unexpected degradation during validation after several recent updates to the data pipeline. Which of the following actions are most appropriate to monitor and troubleshoot the functioning of the data collection, experiments, and software processes?
- A
Enable logging and auditing of data transformations and preprocessors used in the data pipeline.
- B
Retrain the model immediately to see if performance improves without investigating the data pipeline.
- C
Monitor experiment tracking logs to identify recent parameter changes or anomalies.
- D
Use version control for datasets and compare them to earlier versions to identify discrepancies.
- E
Ignore the issue, as performance degradation is expected during model fine-tuning.
Show answer and explanation
Correct answers: A, C, D
Explanation
Monitoring the functioning of the data pipeline, experiments, and software processes is essential to ensure model performance and reliability. By enabling logging, tracking experiments, and maintaining dataset version control, you can identify and address potential issues systematically. Ignoring the issue or retraining blindly does not solve the underlying problem and could lead to wasted resources and suboptimal results.
- A. Correct.
Correct: Enabling logging and auditing helps ensure transparency in data transformations and can help identify any issues introduced in the data pipeline.
- B. Incorrect.
Incorrect: Retraining the model without investigating the cause of performance degradation is a reactive approach and does not address the root cause of the issue.
- C. Correct.
Correct: Monitoring experiment tracking logs is critical for identifying changes in parameters, hyperparameters, or configurations that may impact performance.
- D. Correct.
Correct: Version control for datasets allows you to compare current and prior data, making it easier to detect discrepancies that might affect model training.
- E. Incorrect.
Incorrect: Ignoring performance degradation is not a best practice, as it could lead to undetected bugs or systemic issues in the pipeline.