BigML vs FEDOT

BigML is a machine-learning platform for classification, regression, forecasting, clustering, anomaly detection, association discovery, and topic modeling. It provides interactive model visualizations, prediction explanations, and field-importance views. Models can be exported in JSON PML or PMML formats for use in programming languages and web, mobile, or IoT applications. OptiML helps select and tune models; WhizzML automates workflows, and Scriptify turns workflows into reusable scripts. Users can work through the REST API and language bindings including Python, Node.js, Ruby, Java, and Swift. Listed integrations include Node-RED, Google Sheets, Zapier, Alexa, Docker, and macOS. BigML runs on web, macOS, API, and self-hosted platforms, with private deployments available on a cloud provider, ISP, or on-premises. The free plan is $0/month and includes up to 60 tasks, a 16 MB per-task dataset limit, and two parallel tasks. Paid plans start at $1,000/month; a three-day trial is available.

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.