Labelbox Features

Supports programming languages such as Java, C, or Python. Supports front-end languages such as HTML, CSS, and JavaScript

Offers the ability for developers to drag and drop pieces of code or algorithms when building models

Provides users with pre-built algorithms for simpler model development

Supplies large data sets for training individual models

Transforms raw data into features that better represent the underlying problem to the predictive models

Offers image recognition services

Offers natural language processing services

Offers natural language generation services

Offers artificial neural networks for users

Offers natural language understanding services

Provides deep learning capabilities

Manages the intelligent application for the user, reducing the need of infrastructure

Allows users to insert machine learning into operating applications

Provides easily scaled machine learning applications and infrastructure

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 ability to import a variety of data sources for immediate use

Gives user a metric to determine the quality of data labelers, based on consistency scores, domain knowledge, dynamic ground truth, and more.

Ensures that labeling tasks are accurate through consensus, review, anomaly detection, and more.

Ensures the data is of a high quality as compared to benchmark.

Gives user the ability to review and edit labels.

Uses models to predict the correct label for a given input (image, video, audio, text, etc.).

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.

has the ability to detect objects within images.

Track unique object IDs across multiple video frames

Supports a range of different types of images (satelite, thermal cameras, etc.)

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.

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.

Allows users to generate text based on a text prompt.

Condenses long documents or text into a brief summary.

Allows users to generate text based on a text prompt.

Condenses long documents or text into a brief summary.

Provides the ability to generate images from a text prompt.

Enables smart selection of data for annotation to reduce overall training time and costs.

Allows for automatic retraining of models with newly annotated data for continuous improvement.

Facilitates the setup of an active learning process tailored to specific AI projects.

Allows users to establish a feedback loop between data annotation and model training.

Provides the ability to identify and address edge cases to enhance model robustness.

Enables efficient triaging of training data to identify which data points should be labeled next.

Streamlines the data labeling process with tools designed for efficiency and accuracy.

Automates the detection of anomalies and outliers in the training data for correction.

Offers tools to optimize the selection of data for labeling based on model uncertainty.

Provides actionable insights into data quality, enabling targeted improvements in data labeling.

Delivers in-depth insights into factors impacting model performance and suggests enhancements.

Enables model improvement at the lowest possible cost by focusing on the most impactful data.

Integrates the handling of edge cases into the model training loop for continuous performance enhancement.

Provides the ability to fine-tune models for increased accuracy and specialization for niche use cases.

Offers advanced tools to analyze label outliers and errors to inform further model training.

Capability to perform complex tasks without constant human input

Ability to break down and plan multi-step processes

Works across multiple software systems or databases

Improves performance based on feedback and experience

Engages in human-like conversation for task delegation

Anticipates needs and offers suggestions without prompting

Makes informed choices based on available data and objectives