Amazon SageMaker Autopilot vs FEDOT
Amazon SageMaker Autopilot
aws.amazon.comFEDOT
fedot.readthedocs.ioSide by side
| Figure | Amazon SageMaker Autopilot | FEDOT |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | Yes | — |
| Open source | — | — |
| Web | Yes | — |
| Windows | — | Yes |
| macOS | — | Yes |
| Linux | — | Yes |
| API | Yes | Yes |
| Self-hosted | — | Yes |
| Plans published | 5 | 1 |
| Spec score · AutoML Software | 8.7 (#3) | 8.7 (#4) |
| Purpose | Autopilot automatically creates machine learning models with full visibility and is now in SageMaker Canvas. | FEDOT is an open-source framework for automated modeling and machine learning that builds end-to-end solutions using an evolutionary approach. |
| Support | AWS provides support plans with proactive guidance, issue resolution, and tools. | The maintainers say they are happy to help users adopt FEDOT to their needs. |
Plans
Amazon SageMaker Autopilot
- On-demandPriced on request
- Savings PlansPriced on request
- Amazon SageMaker AI pay-as-you-goFree
- SageMaker AI Free TierFree
- On-demand SageMaker AIPrice not published
In their own words
Amazon SageMaker Autopilot
Amazon SageMaker Autopilot has no write-up on Specifiction yet; its figures are above.
FEDOT
FEDOT is a free, open-source framework for automating the creation of data-driven composite models. It supports classification, regression, clustering and time-series forecasting, with tabular, text and image inputs, including multimodal combinations. Its workflow covers preprocessing, model selection, tuning, cross-validation and serialization. Users can leave parameters unspecified for fuller automation or provide them to guide pipeline composition. FEDOT uses the GOLEM library to optimize graph-based pipelines with meta-heuristic methods. Presets include best_quality, fast_train, stable, auto, gpu, ts and automl; auto is the default. Data can come from CSV files, pandas DataFrames, NumPy arrays or time-series CSV data. It can be installed with pip, and the API can be called from a console without Python code, saving predictions as CSV files. GPU evaluation uses RAPIDS and supports a specified set of models. The project is distributed under the BSD 3-Clause license and supports Windows, Linux and macOS.