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Your geographically distributed data science team is working on a computationally intensive project that requires running multiple experiments. You need a platform that enables effective real-time collaboration and rapid experimentation. To accelerate the experimentation cycle, you plan to add GPUs and want to avoid manual infrastructure setup. What is the Google-recommended approach to achieve this?
A
Configure a managed Dataproc cluster for large-scale data processing. Configure individual Jupyter notebooks on VMs that each team member uses for experimentation and model development.
B
Use Colab Enterprise with Cloud Storage for data management. Use a Git repository for version control.
C
Use Vertex AI Workbench and Cloud Storage for data management. Use a Git repository for version control.
D
Configure a distributed JupyterLab instance that each team member can access on a Compute Engine VM. Use a shared code repository for version control.