From CSV to Machine Learning Model in Minutes
The fastest path from a spreadsheet to a production model: drop the CSV, tell OctOpus the business question, watch the agent profile the data, run experiments, validate the winner, and deploy a prediction API. No environment setup, no notebooks, no devops handoff. Most tabular tasks complete in under 10 minutes.
What works out of the box
Tabular classification — churn, fraud, conversion, default risk. Tabular regression — revenue, LTV, demand, price, score. Time-series forecasting when the CSV has a date column. NLP classification when the CSV has a text column. OctOpus auto-detects which task fits the data and picks the right model family.
What the agent does on first contact
Reads the CSV with safe encoding/separator detection. Profiles columns: type inference, cardinality, missingness, range. Probes for target leakage. Picks a baseline model (CatBoost / LightGBM / TabPFN / Ridge depending on size). Trains and reports holdout metrics. If the baseline is weak, iterates with tuning, deeper models, and stacking.
Deploy
Once a winner is picked, it becomes a Python pickle, a deployable container, AND a hosted prediction API at a dedicated URL. Send rows in, get predictions out. Re-train any time with one click.
Key capabilities
- Drop a CSV → production model in 5-10 minutes (tabular).
- Auto-detects task: classification / regression / forecasting / NLP.
- Holdout validation, leakage probe, model card — all automatic.
- Prediction API hosted at a dedicated URL.
- Free tier handles real production CSVs.