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.

To help you make an informed decision, we've compiled a comprehensive comparison of these two products, delving into their features, pros, cons, pricing, and more. Get ready to explore the nuances that set them apart and determine which one is the perfect fit for your requirements.

Supervisely

Supervisely

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.

Categories:
Ai Tools & Services Computer Vision
nocode annotation neural-networks computer-vision machine-learning

Supervisely Features

  1. Image annotation
  2. Video annotation
  3. 3D annotation
  4. Model training
  5. Model deployment
  6. Collaboration
  7. Version control
  8. Integrations

Pricing

  • Freemium
  • Subscription-Based

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

Amazon SageMaker Data Labeling

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.

Categories:
Ai Tools & Services Machine Learning
machine-learning data-labeling training-data

Amazon SageMaker Data Labeling Features

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

Pricing

  • Pay-As-You-Go

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