<aside> ☝ Like other notes on this site, this note contains only a few noteworthy points of the topic.

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👉 My Github Repo for this note: dinhanhthi/google-vertex-ai

👉 All services needed for Data Science on Google Cloud.

Good to know

• You should choose the same location/region for all services (google project, notebook instances,...). 👉 Check section “Choose the same locations”.
• Troubleshooting.
• Access Cloud Storage buckets.
• Google Cloud Pricing Calculator
• [gcloud ai](<https://cloud.google.com/sdk/gcloud/reference/ai/>) references (for Vertex AI)
• ALways use Logging service to track the problems.
• When making models, especially for serving on prod, don't forget to use logging services.
• When creating a new notebook instance, consider to choose a larger size for "boot disk" (100GB is not enough as it is).
• If you run the gcp command lines in workbench, you don't have to give the credential for the connecting to gcp. It's automatically passed.

Tutorials & references

1. What is Vertex AI? -- Official video.
2. Google Cloud Vertex AI Samples -- Official github repository.
3. Vertex AI Documentation AIO: Samples - References -- Guides.

Notebooks (Workbench)

<aside> ☝ If you are going to create images with docker inside the virtual machine, you should choose more boot disk space (default = 100GB but you should choose more than that). In case you wanna change the size of disk, you can go to Compute Engine / Disks (ref).

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<aside> 🚨 Remember to shutdown the notebook if you don't use it!!

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