https://github.com/zabir-nabil/vertexai-notebook-demos
Notebook demos + tutorials for Vertex AI (training, deployment, explainability, monitoring, etc.)
https://github.com/zabir-nabil/vertexai-notebook-demos
Last synced: 3 months ago
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Notebook demos + tutorials for Vertex AI (training, deployment, explainability, monitoring, etc.)
- Host: GitHub
- URL: https://github.com/zabir-nabil/vertexai-notebook-demos
- Owner: zabir-nabil
- License: mit
- Created: 2022-12-27T06:38:32.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2023-01-03T07:32:57.000Z (almost 3 years ago)
- Last Synced: 2025-03-24T15:22:12.785Z (7 months ago)
- Size: 2.93 KB
- Stars: 5
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# VertexAI-notebook-demos
Notebook demos + tutorials for Vertex AI (training, deployment, explainability, monitoring, etc.)| **Feature / Component** | **Vertex AI** |
|-------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Data ingestion | 👌 Monitoring jobs or SDK |
| Performance monitoring | For online model performance, Vertex AI is pretty limited \(as per docs\)\. They offer only a ‘Prediction error percentage’ which is not well documented as of now\. |
| Feature drift | 👌 Vertex AI has reasonably good support for feature drift\. Alerts can be configured and sent periodically\. |
| Training\-serving skew | 🏆 Vertex AI becomes really useful when we have a continuous process from model training to deployment\. We can detect skewness in training and production data distribution\. |
| Monitors & Alerts | 👌 Vertex AI provides basic monitoring and alerting for drift, and skew\. It’s available for the feature store too\.