Keep complex, unstructured models in check at all times. Unlike models with tabular data, monitoring models with unstructured data can be challenging due to the nature of their high-dimensional vectors.
Watch the video below to learn how Fiddler’s unique cluster-based binning technique enables you to accurately and confidently monitor NLP and CV models.
Models with unstructured data are complex and require techniques that can monitor text and images represented by high-dimensional vectors. Standard model drift metrics, such as Jensen-Shannon divergence (JSD), which are widely used for straightforward tabular models, fall short of monitoring distributional shifts of high-dimensional vectors as a whole.
Fiddler empowers you to accurately monitor NLP and CV models with a patent-pending cluster-based binning algorithm and detect even the slightest distributional shifts of high-dimensional vectors.
Business stakeholders rely on model predictions to make decisions that propel the business forward. However, decisions are only as good as model predictions and model value depends on stakeholder confidence.
Fiddler allows you to make informed business decisions by understanding the “why” behind model outcomes.
Recognize market shifts early and update models before they decay. Ensure models consistently deliver positive business impact.
Fiddler enables you to perform root cause analysis to uncover underperforming segments, compare models, conduct ‘what-if’ analysis to test hypotheses, and measure the impact of feature importance.
Accurately monitor high-dimensional vectors, such as text and images.
Gain contextual insights into complex data drift by locating and identifying drift in high dimensional spaces.
Detect shifts in NLP models caused by changes in meaning and semantics.
Track changes in CV models that may be altered by blurring, low-light, pixelation, etc.