Cloud AutoML vs Supervisely

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

Cloud AutoML is a Ai Tools & Services solution with tags like automl, custom-models, google-cloud, machine-learning.

It boasts features such as Automated machine learning, Pre-trained models, Custom model training, Model deployment, Online prediction, Model monitoring and pros including Easy to use interface, Requires no ML expertise, Scalable, Integrated with other GCP services.

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	Cloud AutoML	Supervisely
Overall Score	N/A	N/A
Primary Category	Ai Tools & Services	Ai Tools & Services

Product Overview

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.

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

Cloud AutoML Features

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

Supervisely Features

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

Pros & Cons Analysis

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

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

Cloud AutoML

• Pay-As-You-Go

Supervisely

• Freemium
• Subscription-Based