Drive Business Impact With High Performing Unstructured Models

Deliver on business KPIs by ensuring natural language processing (NLP) and computer vision (CV) models perform as intended.

Accurately monitor complex models

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.

Quickly pinpoint root cause of model underperformance and drift to improve model outcomes
Accelerate AI time to market

Monitor unstructured models accurately

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 patented cluster-based binning algorithm and detect even the slightest distributional shifts of high-dimensional vectors.

  • Detect shifts in data distribution by comparing baseline and production data
  • Quickly pinpoint root cause of model underperformance and drift to improve model outcomes
  • Accurately quantify the amount of data drift at a given time
Build trust into AI

Boost confidence in complex unstructured models

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.

  • Connect model predictions to business KPIs
  • Increase confidence in unstructured model decisions with explainable AI
  • Gain visibility and transparency in text and image models
Connect model predictions to business KPIs
Interactive 3D UMAP
Adapt quickly

Improve unstructured models as market dynamics shift

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.

  • Gain business intelligence with deep model analytics 
  • Identify feature impact and importance that contributed to changes in model outcomes
  • Gain qualitative insights on how drift has happened in high dimensional spaces by visualizing in the 3D UMAP

NLP and CV model monitoring features

Cluster-based binning algorithm

Accurately monitor high-dimensional vectors, such as text and images.

3D UMAP visualizer

Gain contextual insights into complex data drift by locating and identifying drift in high dimensional spaces.

Natural language processing

Detect shifts in NLP models caused by changes in meaning and semantics.

Computer vision

Track changes in CV models that may be altered by blurring, low-light, pixelation, etc.