Google Cloud Professional Machine Learning Engineer — Question 133

You are an ML engineer responsible for designing and implementing training pipelines for ML models. You need to create an end-to-end training pipeline for a TensorFlow model. The TensorFlow model will be trained on several terabytes of structured data. You need the pipeline to include data quality checks before training and model quality checks after training but prior to deployment. You want to minimize development time and the need for infrastructure maintenance. How should you build and orchestrate your training pipeline?

Answer options

• A. Create the pipeline using Kubeflow Pipelines domain-specific language (DSL) and predefined Google Cloud components. Orchestrate the pipeline using Vertex AI Pipelines.
• B. Create the pipeline using TensorFlow Extended (TFX) and standard TFX components. Orchestrate the pipeline using Vertex AI Pipelines.
• C. Create the pipeline using Kubeflow Pipelines domain-specific language (DSL) and predefined Google Cloud components. Orchestrate the pipeline using Kubeflow Pipelines deployed on Google Kubernetes Engine.
• D. Create the pipeline using TensorFlow Extended (TFX) and standard TFX components. Orchestrate the pipeline using Kubeflow Pipelines deployed on Google Kubernetes Engine.

Correct answer: B

Explanation

The correct answer is B because TensorFlow Extended (TFX) provides a robust framework designed specifically for production-level ML pipelines, ensuring effective data and model quality checks. Options A and C suggest using Kubeflow Pipelines, which is less tailored for TensorFlow models compared to TFX, while option D, although utilizing TFX, suggests an orchestration method that does not leverage Vertex AI Pipelines, which can optimize the deployment process.