V7 Darwin Features

Allows users to input models built in a variety of languages.

Allows users to choose the framework or workbench of their preference.

Records versioning as models are iterated upon.

Provides a way to quickly and efficiently deploy machine learning models.

Offers a way to scale the use of machine learning models across an enterprise.

Allows users to input models built in a variety of languages.

Allows users to choose the framework or workbench of their preference.

Records versioning as models are iterated upon.

Provides a way to quickly and efficiently deploy machine learning models.

Offers a way to scale the use of machine learning models across an enterprise.

Records and organizes all machine learning models that have been deployed across the business.

Tracks the performance and accuracy of machine learning models.

Provisions users based on authorization to both deploy and iterate upon machine learning models.

Allows users to manage model artifacts and tracks which models are deployed in production.

Records and organizes all machine learning models that have been deployed across the business.

Tracks the performance and accuracy of machine learning models.

Provisions users based on authorization to both deploy and iterate upon machine learning models.

Gives user a metric to determine the quality of data labelers, based on consistency scores, domain knowledge, dynamic ground truth, and more. 21 reviewers of V7 Darwin have provided feedback on this feature.

Ensures that labeling tasks are accurate through consensus, review, anomaly detection, and more. This feature was mentioned in 24 V7 Darwin reviews.

As reported in 21 V7 Darwin reviews. Ensures the data is of a high quality as compared to benchmark.

Based on 22 V7 Darwin reviews. Gives user the ability to review and edit labels.

Based on 16 V7 Darwin reviews. Uses models to predict the correct label for a given input (image, video, audio, text, etc.).

Based on 14 V7 Darwin reviews. Automatically route input to the optimal labeler or labeling service based on predicted speed and cost.

Has the ability to place imaginary boxes or polygons around objects or pixels in an image. 27 reviewers of V7 Darwin have provided feedback on this feature.

has the ability to detect objects within images. 24 reviewers of V7 Darwin have provided feedback on this feature.

Track unique object IDs across multiple video frames 17 reviewers of V7 Darwin have provided feedback on this feature.

Supports a range of different types of images (satelite, thermal cameras, etc.) This feature was mentioned in 18 V7 Darwin reviews.

Based on 13 V7 Darwin reviews. Gives user the ability to extract entities from text (such as locations and names).

Gives user the ability to tag text based on its sentiment.

Gives user the ability to label and verify text data in an image. This feature was mentioned in 10 V7 Darwin reviews.

Allows the user to transcribe audio.

Gives user the ability to label emotions in recorded audio.

Control model usage and performance in production

Deploy mission-critical ML applications where and when you need them

Easily compare experiments—code, hyperparameters, metrics, predictions, dependencies, system metrics, and more—to understand differences in model performance.