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Evaluation metrics and KPI tracking
Dynamic leaderboards and model comparison
Champion/candidate testing workflow
Audit-ready evaluation reports
Kiroframe provides an automated evaluation framework that collects and compares model metrics — including accuracy, loss, precision, recall, and more — across training runs, environments, and dataset versions.
With support for custom KPIs and thresholds, teams can benchmark models against production goals and ensure consistency across experiments with the best candidate for production deployment.
Gain instant visibility into model performance with built-in leaderboards that auto-rank models by your selected metrics. Use tags, filters, and versioning to compare experiments across:
Model architectures
Hyperparameter sets
Datasets and environments
The leaderboard view helps you quickly identify top-performing configurations and validate model changes over time.
Support continuous model evaluation using champion/candidate methodology — automatically promoting new model versions if they outperform existing ones under defined conditions.
This option helps data science and MLOps teams deploy with confidence and reduce regression risk in production environments.
Kiroframe generates structured evaluation logs and visual reports for every training run, enabling compliance, reproducibility, and knowledge transfer across teams.
Reports include metric trends, model metadata, and evaluation context — all of which are available via the UI or API.
