Google Professional Machine Learning Engineer Question 191
Single answerGoogle Cloud PlatformYou are leading a team tasked with developing a machine learning model for a recommendation system. During the development phase, your team needs a collaborative environment to prototype models, share code, and experiment with pre-built ML frameworks and tools. Which Google Cloud environment should you choose?
- A
Google Cloud Dataproc
- B
Vertex AI Workbench
- C
Google Kubernetes Engine (GKE)
- D
BigQuery ML
Show answer and explanation
Correct answer: B
Explanation
Vertex AI Workbench is the most suitable choice for collaborative ML development and experimentation. It provides a managed notebook environment with pre-installed ML frameworks and seamless integration with other Google Cloud services, facilitating efficient prototyping and teamwork.
- A. Incorrect.
Google Cloud Dataproc is primarily used for running Apache Hadoop and Spark jobs, which is more suited for large-scale data processing rather than collaborative development of ML models.
- B. Correct.
Vertex AI Workbench provides a managed, collaborative Jupyter Notebook environment that integrates with Google Cloud services and is specifically designed for ML development, experimentation, and prototyping.
- C. Incorrect.
Google Kubernetes Engine (GKE) is a containerized application management system. While it can host ML models, it is not optimized for collaborative development or prototyping.
- D. Incorrect.
BigQuery ML enables you to build and deploy ML models directly within BigQuery using SQL. It is a great tool for ML on structured data but not an environment for collaborative development and experimentation.