BigML
BigML is a machine learning platform that lets teams build predictive models, decision trees, clustering analyses, and anomaly detectors without writing code from scratch. It provides a visual interface for the full ML workflow: data upload, feature engineering, model training, evaluation, and deployment via API.
The problem BigML addresses is accessibility. Most businesses have data that could inform better decisions (customer churn, demand forecasting, defect prediction), but they lack the data science team to build and maintain custom ML models. BigML gives analysts and developers a way to train models through a web interface or API, then deploy predictions into production applications.
At Osher, we use BigML as part of broader AI agent development and automated data processing projects. A common pattern is training a classification or regression model in BigML, then calling its prediction API from an n8n workflow that processes incoming data and routes it based on model output. For example, we have built document classification systems that use ML models to categorise incoming files and route them to the correct team. See our medical document classification case study for a real-world example.
BigML suits organisations that want to apply machine learning to business problems without hiring a full data science team or managing GPU infrastructure.