WhyLabs Features

Deployment (10)

Language Flexibility: Based on 17 WhyLabs reviews. Allows users to input models built in a variety of languages.
Framework Flexibility: As reported in 19 WhyLabs reviews. Allows users to choose the framework or workbench of their preference.
Versioning: Records versioning as models are iterated upon. 17 reviewers of WhyLabs have provided feedback on this feature.
Ease of Deployment: Provides a way to quickly and efficiently deploy machine learning models. This feature was mentioned in 19 WhyLabs reviews.
Scalability: Offers a way to scale the use of machine learning models across an enterprise. This feature was mentioned in 18 WhyLabs reviews.
Language Flexibility: Based on 18 WhyLabs reviews. Allows users to input models built in a variety of languages.
Framework Flexibility: Based on 20 WhyLabs reviews. Allows users to choose the framework or workbench of their preference.
Versioning: Records versioning as models are iterated upon. This feature was mentioned in 17 WhyLabs reviews.
Ease of Deployment: Provides a way to quickly and efficiently deploy machine learning models. 20 reviewers of WhyLabs have provided feedback on this feature.
Scalability: Based on 21 WhyLabs reviews. Offers a way to scale the use of machine learning models across an enterprise.

Cataloging: Records and organizes all machine learning models that have been deployed across the business. 16 reviewers of WhyLabs have provided feedback on this feature.
Monitoring: As reported in 21 WhyLabs reviews. Tracks the performance and accuracy of machine learning models.
Governing: Provisions users based on authorization to both deploy and iterate upon machine learning models. This feature was mentioned in 15 WhyLabs reviews.
Model Registry: As reported in 15 WhyLabs reviews. Allows users to manage model artifacts and tracks which models are deployed in production.
Cataloging: Records and organizes all machine learning models that have been deployed across the business. 15 reviewers of WhyLabs have provided feedback on this feature.
Monitoring: Tracks the performance and accuracy of machine learning models. 20 reviewers of WhyLabs have provided feedback on this feature.
Governing: Provisions users based on authorization to both deploy and iterate upon machine learning models. This feature was mentioned in 16 WhyLabs reviews.

Metrics: Control model usage and performance in production 19 reviewers of WhyLabs have provided feedback on this feature.
Infrastructure management: Deploy mission-critical ML applications where and when you need them This feature was mentioned in 16 WhyLabs reviews.
Collaboration: Based on 18 WhyLabs reviews. Easily compare experiments—code, hyperparameters, metrics, predictions, dependencies, system metrics, and more—to understand differences in model performance.