AIF-C01 Question 52
Select 3You are tasked with deploying a machine learning model to production in AWS. To ensure long-term success, you need to implement an MLOps pipeline that addresses key challenges such as model monitoring, scalability, and minimizing technical debt. Which combination of actions will best achieve this goal?
- A
Set up Amazon SageMaker Model Monitor to track model performance metrics and identify data drift.
- B
Use a manual process to retrain the model periodically based on feedback from application users.
- C
Deploy the model using Amazon SageMaker endpoints with auto-scaling enabled to handle varying traffic loads.
- D
Store experiment results in a centralized location using Amazon SageMaker Experiments for reproducibility.
- E
Rely on the initial training dataset to make long-term decisions without regular updates or re-training.
Show answer and explanation
Correct answers: A, C, D
Explanation
Implementing MLOps in AWS requires a combination of tools and best practices to ensure a scalable, reproducible, and production-ready pipeline. By using Amazon SageMaker Model Monitor, auto-scaling endpoints, and SageMaker Experiments, you can address key operational challenges such as monitoring, scalability, and reproducibility while minimizing technical debt. Avoiding manual processes or outdated data ensures that the solution remains robust and efficient over time.
- A. Correct.
Setting up Amazon SageMaker Model Monitor is essential for tracking model performance and ensuring that the model remains accurate over time by identifying issues such as data drift.
- B. Incorrect.
Using a manual process for model retraining is not scalable and can lead to delays or errors, increasing technical debt.
- C. Correct.
Deploying the model with Amazon SageMaker endpoints and enabling auto-scaling ensures that the system can handle varying traffic loads efficiently, making the pipeline scalable and production-ready.
- D. Correct.
Using Amazon SageMaker Experiments to store experiment results promotes reproducibility and helps manage technical debt by documenting the decisions and parameters used during the model development process.
- E. Incorrect.
Relying solely on the initial training dataset without regular updates or retraining makes the model vulnerable to degradation over time and fails to address production readiness or long-term scalability.