colabel vs. AutoML

What's the difference between colabel and other automation tools? Let's look at how our platform compares to AutoML solutions.

Feature comparison

AutoML

Process capabilities
Build end-to-end processes with complex logic.
Machine learning capabilities
Build and integrate machine learning models to automate data-related processes.
Ease of use
Intuitive software which does not require extensive training.
Accessibility
Cost of the solution, required technical knowledge needed for implementation.
Customization
Build models based on your own data, integrate apps, set personalized actions and triggers.

Legend

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Feature availability
Excellent (fully available)
Good (partially available, e.g. plugins)
Fair (needs customization or limited)
Poor (not available)

colabel = AutoML + 3 x💡

AutoML (automated machine learning) covers the complete pipeline from raw data to deployed machine learning model, without needing to write code. This makes the technology much more accessible – and that's why we made it a core of our software.

We wanted to enable operators to control the whole process as part of their daily work. Therefore, we added three important aspects to the AutoML aspect: An intuitive data labeling solution, best-in-class workflow integration with existing tools, and continuous re-training through human-in-the-loop.

Workflow integration

colabel is built to fit your needs, from workflow entry over models based on your data to where predictions are sent to. Connect to hundreds of integrations or call our API directly.

+ 100's more

Human in the loop

We know that even the best machine learning models make mistakes. Our software is meant to work with the operator, not behind the shades. If it is unsure about something, it will ask for help from the expert – thereby getting better every day.

Try for free and see for yourself

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