01Program dashboard, recent runs, metrics, and lifecycle overview.
ML Experiment NavigatorEXPERIMENT & MODEL LIFECYCLE WORKSPACEA route-driven experiment registry for tracking datasets, parameters, metrics, candidate models, approvals, and reproducible reports with local persistence.
Organize datasets, experiments, model candidates, metrics, and approval evidence into separate reviewable workspaces.
A route-driven experiment registry for tracking datasets, parameters, metrics, candidate models, approvals, and reproducible reports with local persistence.
Dataset registries, experiment runs, model cards, approval states, local persistence, and JSON reports.
LocalStorage supports a single-browser demonstration. Team collaboration requires authentication, shared storage, access control, and audit history.
Every stage is separated so the interface stays readable, reviewable, and useful for AI and Data Science learning.
Record the dataset, task, baseline, and evaluation plan.
Capture algorithms, parameters, metrics, and run status.
Review candidate models against accuracy and governance criteria.
Export a reproducible report containing evidence and approval status.
The dashboard content remains fully interactive and is positioned after the clear project introduction.
Use the route-style workspaces to manage datasets, experiments, models, and decision reports.
Accuracy | robustness | fairness | documentation | rollback plan
Organize datasets, experiments, model candidates, metrics, and approval evidence into separate route-style workspaces. The design demonstrates how navigation and state can make an ML lifecycle easier to review.
LocalStorage is appropriate for a single-browser demo; team collaboration needs authentication and shared persistent storage.
