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neptune.ai Features
What are the features of neptune.ai?
Deployment
- Language Flexibility
- Framework Flexibility
- Versioning
- Scalability
- Language Flexibility
- Framework Flexibility
- Versioning
- Scalability
Management
- Cataloging
- Monitoring
- Governing
- Model Registry
- Monitoring
Operations
- Metrics
- Collaboration
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neptune.ai Categories on G2
Filter for Features
Deployment
Language Flexibility | Allows users to input models built in a variety of languages. This feature was mentioned in 36 neptune.ai reviews. | 87% (Based on 36 reviews) | |
Framework Flexibility | As reported in 38 neptune.ai reviews. Allows users to choose the framework or workbench of their preference. | 93% (Based on 38 reviews) | |
Versioning | Records versioning as models are iterated upon. 39 reviewers of neptune.ai have provided feedback on this feature. | 90% (Based on 39 reviews) | |
Scalability | Offers a way to scale the use of machine learning models across an enterprise. 33 reviewers of neptune.ai have provided feedback on this feature. | 89% (Based on 33 reviews) | |
Language Flexibility | Based on 34 neptune.ai reviews. Allows users to input models built in a variety of languages. | 85% (Based on 34 reviews) | |
Framework Flexibility | Allows users to choose the framework or workbench of their preference. This feature was mentioned in 34 neptune.ai reviews. | 91% (Based on 34 reviews) | |
Versioning | Records versioning as models are iterated upon. 35 reviewers of neptune.ai have provided feedback on this feature. | 90% (Based on 35 reviews) | |
Scalability | As reported in 34 neptune.ai reviews. Offers a way to scale the use of machine learning models across an enterprise. | 88% (Based on 34 reviews) |
Management
Cataloging | Records and organizes all machine learning models that have been deployed across the business. This feature was mentioned in 34 neptune.ai reviews. | 85% (Based on 34 reviews) | |
Monitoring | Tracks the performance and accuracy of machine learning models. This feature was mentioned in 37 neptune.ai reviews. | 91% (Based on 37 reviews) | |
Governing | Provisions users based on authorization to both deploy and iterate upon machine learning models. This feature was mentioned in 33 neptune.ai reviews. | 83% (Based on 33 reviews) | |
Model Registry | Allows users to manage model artifacts and tracks which models are deployed in production. This feature was mentioned in 34 neptune.ai reviews. | 82% (Based on 34 reviews) | |
Cataloging | Based on 29 neptune.ai reviews. Records and organizes all machine learning models that have been deployed across the business. | 82% (Based on 29 reviews) | |
Monitoring | As reported in 30 neptune.ai reviews. Tracks the performance and accuracy of machine learning models. | 90% (Based on 30 reviews) | |
Governing | Based on 29 neptune.ai reviews. Provisions users based on authorization to both deploy and iterate upon machine learning models. | 85% (Based on 29 reviews) |
Operations
Metrics | Control model usage and performance in production 32 reviewers of neptune.ai have provided feedback on this feature. | 83% (Based on 32 reviews) | |
Collaboration | Easily compare experiments—code, hyperparameters, metrics, predictions, dependencies, system metrics, and more—to understand differences in model performance. This feature was mentioned in 32 neptune.ai reviews. | 92% (Based on 32 reviews) |
