Google Cloud Digital LeaderData and AI with Google CloudMedium
A data science team needs a collaborative, managed environment to develop, train, and deploy machine learning models using popular open-source frameworks like TensorFlow and PyTorch. They also require integrated tools for data exploration and experiment tracking. Which Google Cloud product should they use?
- ACloud Dataproc
- BVertex AI Workbench
- CCloud Functions
- DBigQuery
Show answer & explanationAnswer & explanation
Correct answer: B. Vertex AI Workbench
Vertex AI Workbench provides a unified, managed environment for data scientists to develop and experiment with ML models. It supports popular open-source frameworks and offers integrated tools for the entire ML lifecycle, including data exploration and experiment tracking.
Why the other options are wrong
- A. Cloud Dataproc is for big data processing using Apache Spark/Hadoop, not primarily an ML development environment.
- C. Cloud Functions are for serverless event-driven computing, not suitable for ML model development.
- D. BigQuery is a data warehouse for analytics, not an ML development platform.
Vertex AI Workbench
A fully managed, enterprise-ready development environment for machine learning, providing Jupyter notebooks integrated with Google Cloud services.
- Supports open-source ML frameworks (TensorFlow, PyTorch).
- Offers integrated tools for data exploration and experiment tracking.
- Enables collaborative development for data scientists.
Memory trick: Vertex AI Workbench is the 'workbench' for all your ML creations.