For now, keep the default settings and just provide a name for your notebook. Under Advanced Settings you can customize your notebook by specifying the machine type and location, adding GPUs, providing custom containers, and enabling terminal access. Next, select MANAGED NOTEBOOKS, and then NEW NOTEBOOK. So be sure to click the button in the UI to do so. Note that if this is the first time you’re using Vertex AI in a project, you’ll be prompted to enable the Vertex API and the Notebooks API. Under the Vertex AI section of the cloud console, select “Workbench”. You’ll use a few of these products today, starting with Workbench, which is the managed notebook offering. Vertex AI contains lots of different products that help you across the entire lifecycle of an ML workflow. To train and deploy the model, you’ll use Vertex AI, which is Google Cloud’s managed machine learning platform.
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