Google Professional Machine Learning Engineer Question 392
Single answerGoogle Cloud PlatformYou are tasked with building a machine learning pipeline to automate the training, evaluation, and deployment of a model. The pipeline needs to handle large datasets and provide integration with managed services to simplify infrastructure management. Which orchestration framework would best meet these requirements and align with Google Cloud's best practices?
- A
Vertex AI Pipelines
- B
Apache Airflow with Cloud Composer
- C
Kubeflow Pipelines
- D
Cloud Scheduler
Show answer and explanation
Correct answer: A
Explanation
Vertex AI Pipelines is the best choice for building machine learning pipelines in Google Cloud. It is a fully managed service that simplifies the orchestration of ML workflows by providing native integration with other Google Cloud services, such as BigQuery and AI Platform Training. It is optimized for handling large datasets and is designed to align with Google Cloud's best practices for machine learning workflows.
- A. Correct.
Vertex AI Pipelines is a managed service for building end-to-end machine learning workflows. It integrates seamlessly with other Google Cloud services and is optimized for handling large datasets and automating ML tasks.
- B. Incorrect.
Apache Airflow with Cloud Composer is useful for general workflow orchestration but lacks ML-specific capabilities, such as model versioning and deployment, which are critical for this use case.
- C. Incorrect.
Kubeflow Pipelines is a good option for ML workflows, but it requires more operational overhead compared to the managed Vertex AI Pipelines. Vertex AI Pipelines is recommended for use cases where simplicity and integration with Google Cloud services are key.
- D. Incorrect.
Cloud Scheduler is a lightweight tool for scheduling cron jobs and is not designed for orchestrating complex machine learning pipelines.