Supervisely vs Amazon SageMaker Data Labeling

Struggling to choose between Supervisely and Amazon SageMaker Data Labeling? Both products offer unique advantages, making it a tough decision.

Supervisely is a Ai Tools & Services solution with tags like nocode, annotation, neural-networks, computer-vision, machine-learning.

It boasts features such as Image annotation, Video annotation, 3D annotation, Model training, Model deployment, Collaboration, Version control, Integrations and pros including No-code platform, Streamlines computer vision workflows, Robust annotation capabilities, Built-in model training, Team collaboration features, Integrates with popular frameworks.

On the other hand, Amazon SageMaker Data Labeling is a Ai Tools & Services product tagged with machine-learning, data-labeling, training-data.

Its standout features include Automated data labeling with pre-built algorithms, Access to on-demand workforce for data labeling, Integration with Amazon SageMaker for training models, Support for image, text, and video labeling, Management console to track labeling progress, API access for custom labeling workflows, and it shines with pros like Reduces time spent labeling datasets, Scales to large datasets with on-demand workforce, Tight integration with Amazon SageMaker simplifies model building workflow, Supports common data types like images, text and video out of the box, Console provides visibility into labeling progress and costs.

Feature	Supervisely	Amazon SageMaker Data Labeling
Overall Score	N/A	N/A
Primary Category	Ai Tools & Services	Ai Tools & Services

Product Overview

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.

Amazon SageMaker Data Labeling

Description: Amazon SageMaker Data Labeling is a service that makes it easy to label your datasets for machine learning. You can request human labelers from a pre-qualified workforce and manage them at scale.

Key Features Comparison

Supervisely Features

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

Amazon SageMaker Data Labeling Features

• Automated data labeling with pre-built algorithms
• Access to on-demand workforce for data labeling
• Integration with Amazon SageMaker for training models
• Support for image, text, and video labeling
• Management console to track labeling progress
• API access for custom labeling workflows

Pros & Cons Analysis

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

Amazon SageMaker Data Labeling

Pros

• Reduces time spent labeling datasets
• Scales to large datasets with on-demand workforce
• Tight integration with Amazon SageMaker simplifies model building workflow
• Supports common data types like images, text and video out of the box
• Console provides visibility into labeling progress and costs

Cons

• Limited to AWS ecosystem
• Data labeling quality dependent on workforce skills
• Algorithms may not produce high quality training data
• Additional costs for data labeling workforce

Pricing Comparison

Supervisely

• Freemium
• Subscription-Based

Amazon SageMaker Data Labeling

• Pay-As-You-Go