The fastest way to build production ML models, agentically.
Upload your data. Describe your goal. Walk away. Come back to deployed models, ranked experiments, and a notebook that explains every decision.
HOW IT WORKS
From raw data to a deployed modelin seven agent-driven phases.
01 / 07
Upload your data.Let the agent plan the work.
1.0 INGEST — real workspace preview
01 — CHAT
Talk to your data like a colleague. Voice, text, or keyboard — agent understands.
Ask in plain English. Watch tool calls stream in real time as the agent reads your tables, proposes transformations, and explains its reasoning.
⌘K to open chat in any tab02 — PLAN
Turn intent into a training plan. Radio buttons, not prompt engineering.
Four to five cards constrain the plan before training begins — target column, task type, compute budget, interpretability preference. Each answer narrows the model candidates, CV strategy, and feature pipeline the planner will execute.
Enter to advance03 — NOTEBOOK
A real notebook, not a pipeline. Pandas, sklearn, Plotly — every cell editable.
Every preprocessing step, feature transform, and model fit lands as a Jupyter cell with real sklearn and pandas code. Edit a line, re-run the cell, or drop in your own — the kernel is yours.
shift+enter to runECOSYSTEM