Vertex AI on Google Distributed Cloud (GDC) air-gapped brings the power of a machine learning platform to your secure on-premises environments. This solution is designed for organizations with strict data sovereignty, security, and privacy requirements. Vertex AI services let you innovate with AI features while maintaining complete control in the same air-gapped infrastructure.
Key features
Vertex AI on Distributed Cloud offers you the following features:
- Air-gapped deployment: Run Vertex AI services entirely within your data center, ensuring data sovereignty and compliance.
- Familiar Vertex AI experience: Take advantage of the same tools and APIs from Google Cloud, simplifying development and management.
- Pre-built models and algorithms: Access a range of pre-trained models for common machine learning tasks, accelerating your time to value.
- Custom model training: Train your machine learning models using your own data, tailoring solutions to your specific needs.
- MLOps capabilities: Streamline your machine learning workflows with tools for model deployment, monitoring, and management.
Available services
Vertex AI on Distributed Cloud offers the following services:
- Online Prediction: (Preview) Deploy and make requests to your prediction models.
- Optical Character Recognition (OCR): Extract text from images and documents.
- Speech-to-Text: Convert spoken language into written text.
- Vertex AI Translation: Translate text between multiple languages.
- Vertex AI Workbench: Create a managed JupyterLab notebook environment for machine learning development.
Benefits
Vertex AI on Distributed Cloud offers the following benefits:
- Seamless development experience: Use the same tools, APIs, and workflows of Vertex AI on Google Cloud, making development and management intuitive and efficient.
- Enhanced security and privacy: Maintain complete control over your data and comply with regulatory requirements.
- Increased agility: Develop and deploy machine learning models in your air-gapped environment.
- Accelerated time to value: Utilize pre-trained models for common machine learning tasks, or train your custom models using your unique datasets.
- Streamlined MLOps: Benefit from robust machine learning operation capabilities for seamless model deployment, monitoring, and management within your air-gapped environment.
Getting started
To get started with Vertex AI on Distributed Cloud, do the following:
- Learn about essential roles and permissions for available services.
- Set up a project for your AI and machine learning workflows.
- Provision GPUs and enable the Vertex AI services.
- Install the Vertex AI client libraries.
Then, you can start building and deploying your machine learning models.