Google Cloud Platform (GCP) provides several options for running Jupyter Notebooks on its cloud infrastructure. Jupyter Notebooks are popular for data analysis, machine learning, and scientific computing due to their interactive and flexible nature. Here are some ways to run Jupyter Notebooks on GCP:

1. AI Platform Notebooks:

   • AI Platform Notebooks is a managed Jupyter Notebook service provided by GCP. It allows you to create and run Jupyter Notebooks in a fully managed environment.
   • Features include integration with Google Cloud Storage, support for custom dependencies, and easy access to Google Cloud’s AI and data services.
   • AI Platform Notebooks offers built-in collaboration and version control features, making it suitable for data science teams.

2. Google Colab:

   • Google Colab, short for Colaboratory, is a free, cloud-hosted Jupyter Notebook environment provided by Google. It is part of Google Drive and is often used for machine learning and data analysis.
   • Colab provides access to free GPU and TPU (Tensor Processing Unit) resources for accelerated computations.
   • You can easily share Colab notebooks with others and collaborate in real-time.

3. Compute Engine:

   • You can create and configure virtual machine instances (VMs) on Google Compute Engine (GCE) to run Jupyter Notebooks.
   • Install Jupyter Notebook and any required dependencies on the VM, and then access the notebook using a web browser by configuring SSH tunneling for secure access.

4. Kubernetes and Kubeflow Pipelines:

   • For advanced users, you can deploy Jupyter Notebooks on Kubernetes clusters using tools like Kubeflow. This approach is suitable for running notebooks at scale in containerized environments.

5. Datalab (Deprecated):

   • Note that Google Cloud Datalab, which was previously used for running Jupyter Notebooks on GCP, is now deprecated. Google recommends using AI Platform Notebooks instead.