Prodigy ML vs Supervisely

Struggling to choose between Prodigy ML and Supervisely? Both products offer unique advantages, making it a tough decision.

Prodigy ML is a Ai Tools & Services solution with tags like machine-learning, data-labeling, computer-vision, nlp.

It boasts features such as Active learning to prioritize labeling, Pre-built templates for common tasks, Real-time model evaluation, Team collaboration, API access, Integrations with popular ML frameworks and pros including Speeds up model training, Reduces need for large labeled datasets, Intuitive interface, Works for image, text, audio and other data types.

On the other hand, Supervisely is a Ai Tools & Services product tagged with nocode, annotation, neural-networks, computer-vision, machine-learning.

Its standout features include Image annotation, Video annotation, 3D annotation, Model training, Model deployment, Collaboration, Version control, Integrations, and it shines with pros like No-code platform, Streamlines computer vision workflows, Robust annotation capabilities, Built-in model training, Team collaboration features, Integrates with popular frameworks.

Feature	Prodigy ML	Supervisely
Overall Score	N/A	N/A
Primary Category	Ai Tools & Services	Ai Tools & Services

Product Overview

Prodigy ML

Description: Prodigy ML is an annotation tool that helps train machine learning models faster. It allows users to rapidly label datasets and build accurate models with less data.

Supervisely

Description: Supervisely is a no-code platform for computer vision and machine learning. It allows users to annotate data, train neural networks, and deploy models without coding. Supervisely streamlines computer vision workflows.

Key Features Comparison

Prodigy ML Features

• Active learning to prioritize labeling
• Pre-built templates for common tasks
• Real-time model evaluation
• Team collaboration
• API access
• Integrations with popular ML frameworks

Supervisely Features

• Image annotation
• Video annotation
• 3D annotation
• Model training
• Model deployment
• Collaboration
• Version control
• Integrations

Pros & Cons Analysis

Prodigy ML

Pros

• Speeds up model training
• Reduces need for large labeled datasets
• Intuitive interface
• Works for image, text, audio and other data types

Cons

• Limited free plan
• Steep learning curve for advanced features
• No offline usage

Supervisely

Pros

• No-code platform
• Streamlines computer vision workflows
• Robust annotation capabilities
• Built-in model training
• Team collaboration features
• Integrates with popular frameworks

Cons

• Steep learning curve
• Limited customization without coding
• No on-premise deployment option

Pricing Comparison

Prodigy ML

• Freemium
• Subscription-Based

Supervisely

• Freemium
• Subscription-Based