Data management
Ingested data can be stored and prepped, with structures like data lakehouses available to handle large amounts of data from disparate sources.
Cat avg: 7.6
Top Performing Features
Automated model training
After teams begin the process, model training can continue autonomously, enabling faster deployment.
Cat avg: 7.6
Model deployment
After training is complete, models can be integrated into business applications using API endpoints or other convenient, standard methods.
Cat avg: 7.9
Machine learning frameworks
A wide variety of machine learning frameworks are available and to be used when training models.
Cat avg: 7.6
Worst Performing Features
Managed scaling
The platform provides the computing resources needed when they’re needed, allowing users to scale training and use up or down.
Cat avg: 7.3
Data monitoring and version control
Teams can track which data is used in training at which point and roll back to previous versions as needed.
Cat avg: 7
Security and compliance
End to end encryption, GDPR compliance, SSO, role-based permissioning, and other precautions are available to protect proprietary business data.
Cat avg: 7.9
Vertex AI Features from Reviews
AI Development
AI Development Platforms include features that focus on data ingestion and preparation, AI model and framework availability, scale, and security.
8.6+13%
Machine learning frameworks
A wide variety of machine learning frameworks are available and to be used when training models.
Category average: 7.6
Data management
Ingested data can be stored and prepped, with structures like data lakehouses available to handle large amounts of data from disparate sources.
Category average: 7.6
Data monitoring and version control
Teams can track which data is used in training at which point and roll back to previous versions as needed.
Category average: 7
Automated model training
After teams begin the process, model training can continue autonomously, enabling faster deployment.
Category average: 7.6
Managed scaling
The platform provides the computing resources needed when they’re needed, allowing users to scale training and use up or down.
Category average: 7.3
Model deployment
After training is complete, models can be integrated into business applications using API endpoints or other convenient, standard methods.
Category average: 7.9
Security and compliance
End to end encryption, GDPR compliance, SSO, role-based permissioning, and other precautions are available to protect proprietary business data.
Category average: 7.9
Vertex AI Features from the Vendor
Additional Features
Vendor-contributedAccess to Gemini, a multimodal model from Google DeepMind
Generative AI models and tools
Open and integrated AI platform
MLOps for predictive and generative AI
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