Labelbox Features

Model Development (5)

Language Support: Supports programming languages such as Java, C, or Python. Supports front-end languages such as HTML, CSS, and JavaScript
Drag and Drop: Offers the ability for developers to drag and drop pieces of code or algorithms when building models
Pre-Built Algorithms: Provides users with pre-built algorithms for simpler model development
Model Training: Supplies large data sets for training individual models
Feature Engineering: Transforms raw data into features that better represent the underlying problem to the predictive models

Managed Service: Manages the intelligent application for the user, reducing the need of infrastructure
Application: Allows users to insert machine learning into operating applications
Scalability: Provides easily scaled machine learning applications and infrastructure
Language Flexibility: Allows users to input models built in a variety of languages.
Framework Flexibility: Allows users to choose the framework or workbench of their preference.
Versioning: Records versioning as models are iterated upon.
Ease of Deployment: Provides a way to quickly and efficiently deploy machine learning models.
Scalability: Offers a way to scale the use of machine learning models across an enterprise.
Language Flexibility: Allows users to input models built in a variety of languages.
Framework Flexibility: Allows users to choose the framework or workbench of their preference.
Versioning: Records versioning as models are iterated upon.
Ease of Deployment: Provides a way to quickly and efficiently deploy machine learning models.
Scalability: 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.
Monitoring: Tracks the performance and accuracy of machine learning models.
Governing: Provisions users based on authorization to both deploy and iterate upon machine learning models.
Model Registry: 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.
Monitoring: Tracks the performance and accuracy of machine learning models.
Governing: Provisions users based on authorization to both deploy and iterate upon machine learning models.

Labeler Quality: Gives user a metric to determine the quality of data labelers, based on consistency scores, domain knowledge, dynamic ground truth, and more. This feature was mentioned in 27 Labelbox reviews.
Task Quality: As reported in 27 Labelbox reviews. Ensures that labeling tasks are accurate through consensus, review, anomaly detection, and more.
Data Quality: Ensures the data is of a high quality as compared to benchmark. 28 reviewers of Labelbox have provided feedback on this feature.
Human-in-the-Loop: Gives user the ability to review and edit labels. This feature was mentioned in 26 Labelbox reviews.

Image Segmentation: Based on 26 Labelbox reviews. Has the ability to place imaginary boxes or polygons around objects or pixels in an image.
Object Detection: has the ability to detect objects within images. 25 reviewers of Labelbox have provided feedback on this feature.
Object Tracking: Track unique object IDs across multiple video frames This feature was mentioned in 24 Labelbox reviews.
Data Types: Based on 24 Labelbox reviews. Supports a range of different types of images (satelite, thermal cameras, etc.)

Model Training Efficiency: Enables smart selection of data for annotation to reduce overall training time and costs.
Automated Model Retraining: Allows for automatic retraining of models with newly annotated data for continuous improvement.
Active Learning Process Implementation: Facilitates the setup of an active learning process tailored to specific AI projects.
Iterative Training Loop Creation: Allows users to establish a feedback loop between data annotation and model training.
Edge Case Discovery: Provides the ability to identify and address edge cases to enhance model robustness.

Smart Data Triage: Enables efficient triaging of training data to identify which data points should be labeled next.
Data Labeling Workflow Enhancement: Streamlines the data labeling process with tools designed for efficiency and accuracy.
Error and Outlier Identification: Automates the detection of anomalies and outliers in the training data for correction.
Data Selection Optimization: Offers tools to optimize the selection of data for labeling based on model uncertainty.
Actionable Insights for Data Quality: Provides actionable insights into data quality, enabling targeted improvements in data labeling.

Model Performance Insights: Delivers in-depth insights into factors impacting model performance and suggests enhancements.
Cost-Effective Model Improvement: Enables model improvement at the lowest possible cost by focusing on the most impactful data.
Edge Case Integration: Integrates the handling of edge cases into the model training loop for continuous performance enhancement.
Fine-tuning Model Accuracy: Provides the ability to fine-tune models for increased accuracy and specialization for niche use cases.
Label Outlier Analysis: Offers advanced tools to analyze label outliers and errors to inform further model training.

Autonomous Task Execution: Capability to perform complex tasks without constant human input
Multi-step Planning: Ability to break down and plan multi-step processes
Cross-system Integration: Works across multiple software systems or databases
Adaptive Learning: Improves performance based on feedback and experience
Natural Language Interaction: Engages in human-like conversation for task delegation
Proactive Assistance: Anticipates needs and offers suggestions without prompting
Decision Making: Makes informed choices based on available data and objectives