Build, evaluate, and refine machine learning models in a reproducible environment.
A structured workflow
Upload datasets, define feature types, validate structure, and run profiling to understand compatibility and data quality.
Select embedding, clustering, or supervised methods. Run on feature or embedded space and enhance with stability and interpretability tools.
Inspect projections, per-label statistics, stability metrics, and feature contributions in one coherent workspace.
Compare runs across models, parameters, and dataset versions. Visualize performance across aggregated metric pillars.
Prune features, filter observations, adjust parameters, and iterate over time while preserving full lineage and reproducibility.
Structured experimentation only matters if it can be preserved, revisited, and defended. Ounias is designed for long-term modeling, not one-off runs.
Lineage trees and dataset versioning preserve every iteration. Models evolve without losing their history.
Drag-and-drop interface makes it easy to compare artifacts.
Execution environments are user-isolated. Your data remains yours.
Every run is seeded, versioned, and measurable. Results can be compared and defended.
Ounias starts at $24/month. Add advanced methods, stability analysis, and extended compute as needed.
Ounias is evolving — here's what we're working on:
Generate interactive, shareable artifacts built from run lineage. Preserve modeling decisions for audit, publication, and collaboration — without exposing raw datasets.
Track and model structural evolution over time. Compare dataset versions and observe how patterns mature as new data accumulates.
Interact with structured run outputs using guided language interfaces. Explore metrics and comparisons without writing custom scripts.
One-click notion exports, OneDrive file integration, and more.