Professional Machine Learning Engineer Exam QuestionsBrowse all questions from this exam
Go to Exam

Professional Machine Learning Engineer Exam - Question 259

You have developed an application that uses a chain of multiple scikit-learn models to predict the optimal price for your company’s products. The workflow logic is shown in the diagram. Members of your team use the individual models in other solution workflows. You want to deploy this workflow while ensuring version control for each individual model and the overall workflow. Your application needs to be able to scale down to zero. You want to minimize the compute resource utilization and the manual effort required to manage this solution. What should you do?

Show Answer
Correct Answer: C

To ensure version control for each individual model while minimizing compute resource utilization and manual management effort, you should expose each individual model as an endpoint in Vertex AI Endpoints and use Cloud Run to orchestrate the workflow. Vertex AI Endpoints provide a managed service for deploying and versioning machine learning models, ensuring consistent version control. Cloud Run enables serverless computing, which allows automatic scaling, including scaling down to zero when not in use, thus optimizing resource utilization. This approach achieves the balance between efficient model management and cost-effective resource usage.

Discussion

4 comments
Sign in to comment
pikachu007Option: C
Jan 13, 2024

Option A: A custom container endpoint for orchestration adds complexity and management overhead. Option B: Loading model files directly into a custom container endpoint can lead to versioning challenges and potential conflicts if models are shared across workflows. Option D: Using BigQuery for model versioning is not its primary function and might introduce complexities in model loading and management.

guilhermebutzkeOption: C
Feb 16, 2024

My Answer: C B and D: Not Correct: Big query is not the best approach to trach versions of model. A and C: Looking for “ensuring version control for each individual mode” (endpoints), and “be able to scale down to zero”, “minimize the compute resource utilization and the manual effort required to manage this solution”, I think to use Cloud Run could be the best option for those cases. https://www.youtube.com/watch?v=nhwYc4StHIc&ab_channel=GoogleCloudTech

AzureDP900Option: C
Jul 5, 2024

Option C is right because: 1)Exposing individual models as Vertex AI Endpoints (Option C) allows for version tracking, which is essential for maintaining consistency across different workflows. 2)Using Cloud Run to orchestrate the workflow (Option C) enables you to scale down to zero and minimize compute resource utilization. 3)You want to deploy your application while ensuring version control for each individual model and the overall workflow.

gscharlyOption: C
Apr 20, 2024

B,D not correct since BQ is not the best approach. A would require more manual work