The rigourous testing platform for AI: Moving beyond aggregate metrics to systematic model validation.
Kolena is a sophisticated ML testing and evaluation platform designed to solve the 'aggregate metrics' fallacy in machine learning. While traditional metrics like global F1-score or Accuracy provide a macro view, they often mask critical model failures in specific data subsets or edge cases. Kolena's technical architecture allows AI teams to define 'Quality Standards' by systematically slicing datasets into granular scenarios (e.g., 'pedestrians at night' vs 'pedestrians in rain' for autonomous driving). By 2026, Kolena has established itself as the industry standard for high-stakes AI deployments, offering a framework for regression testing, dataset hygiene, and model behavior analysis. It enables a 'unit testing' paradigm for AI, where models are validated against specific, reproducible test cases before deployment. The platform supports diverse modalities including computer vision, natural language processing, and complex multi-modal LLM chains, ensuring that model updates do not introduce regressions in critical performance slices.
A framework for defining minimum performance thresholds for specific data slices that must be met before a model can be promoted to production.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
High-performance visualization engine capable of rendering millions of predictions alongside ground truth and metadata.
Uses unsupervised learning to cluster data and automatically identify subsets where the model is underperforming.
Specialized evaluation suite for LLMs focusing on grounding, faithfulness, and safety across varied prompts.
Correlates model performance against any arbitrary metadata (e.g., sensor type, user demographic, weather condition).
Identifies labels that are inconsistent, noisy, or missing across the training and test sets.
Side-by-side performance comparison of multiple model versions across identical test suites.
Model performs well on average but fails to detect cyclists at night in heavy rain.
Registry Updated:2/7/2026
Radiology AI has bias toward specific scanner manufacturers.
Fraud model is less accurate for cross-border transactions than domestic ones.