FEDOT

What it is

FEDOT is an AutoML framework that automates the design of composite modeling pipelines. It supports classification, regression, and time series forecasting tasks on various types of data.

FEDOT stands out by providing an automated approach to designing composite modeling pipelines, particularly excelling in handling multi-modal datasets and time series forecasting.

When it fits — and when it doesn’t

Known weaknesses

Each one links to the public issue, changelog or docs page it came from, where we have one.

1. 01

   Limited algorithm support compared to other AutoML frameworks

   FEDOT's library of algorithms is not as extensive as those found in competitors like H2O or TPOT.

2. 02

   Poor documentation and community support

   The official documentation lacks comprehensive examples and troubleshooting guides, making it difficult for new users to get started.

3. 03

   Performance issues with large datasets

   FEDOT can struggle with memory management when processing very large datasets, leading to slower execution times or crashes.

4. 04

   Complex setup and configuration

   Setting up FEDOT requires a significant amount of configuration, including specifying the correct dependencies and environment settings.

What it does well

• Automated design of composite modeling pipelines

• Support for classification, regression, and time series forecasting tasks

• Handling of multi-modal datasets

Pricing

Free tier

YesOpen source — free to use

Paid from

$0

Model

Flat rate

Enterprise plan

No

Pricing not independently confirmed — check the vendor’s page.