BigML

Hosted machine learning platform with a visual, API-first workflow

AI Data AnalysisFree planOverseasβ˜…β˜…β˜…β˜†β˜† 3.0

What is BigML?

BigML is a hosted machine learning platform built around a resource model: datasets, models, ensembles, evaluations and predictions are all objects with URLs, reachable from a web interface, a REST API, bindings in several languages or a command line tool. That uniformity makes automation straightforward - a script can create a dataset, train an ensemble, evaluate it and produce predictions with the same pattern throughout. AutoML, anomaly detection, time series forecasting and topic modelling are available without configuration. It suits teams that want predictive capability through an API without running their own training infrastructure.

Last updated: 2026-09-20. This site only provides an index; for exact features, pricing, and licensing, see the official website.

Key features

  • Visual interface plus REST API for every model resource
  • Automated model selection and ensembles for tabular data
  • Anomaly detection, clustering and time series forecasting
  • Bindings for Python, Node.js, Java, Ruby and a command line client
  • WhizzML scripting language for reusable machine learning workflows
  • Model export and predictions served through a REST endpoint

Pros & cons

Strengths

  • Consistent API design makes automation predictable
  • Covers common tabular tasks without infrastructure work
  • Free tier is usable for evaluation and small projects

Watch out for

  • Deep learning and text or image models are not its focus
  • Interface feels dated next to newer platforms
  • Usage-based credit pricing needs monitoring on larger projects

Best for & use cases

api-driven predictions, tabular automl, forecasting and anomaly detection

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FAQ

What is the free tier good for?

Learning the platform, prototyping a model on a modest dataset and running low-volume predictions. Credits are consumed by training and prediction calls, so a large dataset can exhaust the monthly allowance quickly.

How does the API-first design help?

Every artefact is a resource with an identifier, so creating a model and scoring new rows is a sequence of standard HTTP calls. That makes it easy to embed predictions in an application without a separate serving layer.

Where does it fall short?

It is not a deep learning platform. Image, audio and large text models sit outside its core, and teams needing those usually pair it with a framework-based pipeline instead.