Renumics GmbH vs Teachable Machine

Renumics GmbH is a Karlsruhe-based industrial AI company that builds systems for analyzing test, machine, and simulation data. They partner with automotive, aerospace, machinery, and pharma teams to move product development toward data-driven, end-to-end workflows.

Their product Onion is an agentic AI assistant for engineering data analysis. Engineers ask questions in natural language to query logging and fleet data, run plausibility checks, detect swapped channels, and generate interactive visualizations without waiting on custom reports from data specialists.

Renumics also maintains Spotlight, an open source toolkit for exploring unstructured datasets including audio, images, video, time-series, and 3D geometry. Beyond software, the company delivers workshops, data readiness checks, proof-of-concept builds, and full custom AI system deployments.

Teachable Machine is a browser-based machine learning trainer from Google that lets you build image, sound, and pose classifiers without writing code. You gather examples into classes, train a model in the browser, then export it as a real TensorFlow.js model for sites, apps, or hardware projects. The whole workflow runs in three steps: gather samples, train, and export.

Most ML platforms assume you know Python and cloud infrastructure. Teachable Machine deliberately targets people who want a working classifier in minutes, not a data science pipeline. Training can stay entirely on your device, so webcam and microphone data never has to leave your computer. The export path points at creative tools like p5.js, Glitch, and Arduino, not enterprise dashboards.

Classroom teachers use it for bias and ethics lessons, students prototype game controllers and assistive devices, and hobbyists build classifiers before touching TensorFlow code. It is free, requires no account to start, and the original 2017 version is still available for camera-only experiments.