Supervisely vs Cloud AutoML

Struggling to choose between Supervisely and Cloud AutoML? 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, Cloud AutoML is a Ai Tools & Services product tagged with automl, custom-models, google-cloud, machine-learning.

Its standout features include Automated machine learning, Pre-trained models, Custom model training, Model deployment, Online prediction, Model monitoring, and it shines with pros like Easy to use interface, Requires no ML expertise, Scalable, Integrated with other GCP services.

Feature	Supervisely	Cloud AutoML
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.

Cloud AutoML

Description: Cloud AutoML is a suite of machine learning products from Google Cloud that enables developers with limited machine learning expertise to train custom models specific to their business needs.

Key Features Comparison

Supervisely Features

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

Cloud AutoML Features

• Automated machine learning
• Pre-trained models
• Custom model training
• Model deployment
• Online prediction
• Model monitoring

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

Cloud AutoML

Pros

• Easy to use interface
• Requires no ML expertise
• Scalable
• Integrated with other GCP services

Cons

• Limited flexibility compared to coding ML from scratch
• Less control over model hyperparameters
• Only available on GCP

Pricing Comparison

Supervisely

• Freemium
• Subscription-Based

Cloud AutoML

• Pay-As-You-Go