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Enterprise Layer Intelligence
Categories/AI Orchestration & MLOps/BigML, Inc
BigML, Inc

BigML, Inc

Founded 2011

Build, export, and automate machine learning models from your data

Pricing model

Free Tier, Paid

You can use BigML to build, evaluate, and deploy machine learning models without complex engineering setups. It lets you import datasets, create decision trees, run cluster analyses, and process images through an interactive web interface or API. You can automate repetitive workflows using its WhizzML domain-specific language and export trained models to run locally on your own servers. The service also supports team collaboration, private deployments, and prediction monitoring to help you turn raw business data into actionable forecasts and automated decisions.

What BigML, Inc does

Import structured datasets from CSV or cloud storageTrain decision trees and random forest classifiersGenerate batch predictions for existing database recordsExport trained models as self-contained code packagesWrite custom automated workflows with WhizzML scriptsMonitor model evaluation metrics and confusion matricesConfigure private deployments inside virtual private cloudsBuild interpretable and exportable machine learning modelsAutomate workflows with custom domain scripting using WhizzMLProcess and categorize image datasetsExport models to execute offline or in edge environmentsDeploy models privately on your own cloud infrastructureManage shared datasets and collaborate with team membersIntegrate predictions directly into apps via REST API

Pricing breakdown

Pricing model: Free tier, Paid

PlanList price
FREEFree
Bronze Enterprise (paid yearly)$45,000 / yr total
BigML Lite (paid yearly)$10,000 / yr total
BigML Lite$1,000 / mo

Frequently asked

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BigML, Artificial Intelligence and Machine Learning, machine learning models, WhizzML, predictive modeling, decision trees, data classification, regression models, anomaly detection, cluster analysis, image processing, model deployment, automated machine learning, MLOps, private deployments, interpretable models