BigML is a machine learning platform from the company of the same name, founded in 2011 and based in Corvallis, Oregon (USA). Through a web interface or an API, users can build models such as decision trees, ensembles, clustering or anomaly detection without much coding. It emphasises interpretable models and automatable workflows. It is offered as a cloud service and for running in one's own data centre (as of October 2026).
DataRobot is a US software company whose platform helps organisations build, operate and monitor AI applications. It became known for automated machine learning (AutoML), which trains and compares many model variants automatically – for example to predict customer churn or demand. Today the company also focuses on running and governing AI agents (as of October 2026).
KNIME is data analytics software in which workflows are assembled visually from building blocks (“nodes”) instead of being programmed. The open-source KNIME Analytics Platform covers data import, cleansing, analysis, machine learning and reporting, while the vendor's commercial offerings add collaboration and operation within organisations. A typical example is a workflow that merges monthly sales data from several Excel files, cleans it and produces a forecast.
No-code refers to tools that let people build applications, automations or websites through graphical interfaces without writing program code. Business teams can implement and test processes themselves instead of waiting for IT development. Limits appear with very specialised requirements, large data volumes or deep integrations. Example: a sales team uses drag and drop to build a flow that sorts and summarises new enquiries.
Machine learning is a branch of artificial intelligence in which computers derive patterns from data instead of having every rule explicitly programmed. Depending on the task, models learn from labelled examples (supervised), from unlabelled data (unsupervised) or through rewards (reinforcement). Applications range from spam filters and demand forecasts to product recommendations. Quality depends heavily on the quantity and quality of the training data.
