Drive Business Impact With High-Performing Unstructured AI Models (NLP & CV)
Monitor Unstructured AI Models with Confidence
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.
End-to-End AI Observability for Unstructured Models
The Fiddler AI Observability and Security Platform allows enterprises to gain full visibility and control over unstructured AI, providing everything from real-time monitoring of NLP and CV models to building trust and continuously optimizing performance.
Track NLP and CV Models Using Vector Monitoring
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, enabling precise vector monitoring and detecting 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
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
Optimize 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
Key Features for Monitoring NLP and CV Models in Unstructured AI
Frequently Asked Questions About NLP and CV Models
What is the difference between CV and ML?
Machine learning (ML) is a broad field that enables systems to learn from data and improve over time without being explicitly programmed. Computer vision (CV) is a specialized area within ML that focuses on allowing machines to interpret and analyze visual information from images or video. CV models are a subset of ML models designed specifically for processing visual data.
What is the integration of CV and NLP?
The integration of computer vision (CV) and natural language processing (NLP) combines visual and textual data to power multimodal AI systems. Examples include image captioning, visual question answering, and content moderation tools that understand text and images. Together, CV monitoring and NLP tracking ensure these hybrid systems remain accurate and reliable in production.
What is an example of data in CV and NLP?
In computer vision, data often includes images, videos, or pixel arrays. In NLP, data consists of text such as documents, emails, chat logs, or transcripts. Combined datasets may include an image with a corresponding description or a video paired with spoken dialogue — essential inputs for training a robust NLP or CV model.
What models are used in computer vision?
Popular computer vision models include convolutional neural networks (CNNs), vision transformers (ViTs), and object detection models like YOLO and Faster R-CNN. These CV models support a range of tasks, such as image classification, object detection, and facial recognition, within AI applications.
What is unstructured AI?
Unstructured AI refers to systems that analyze and learn from unstructured data such as text, images, audio, or video. This type of AI is crucial for businesses using AI for unstructured data, where insights must be extracted from formats that lack predefined structure.
What is an example of unstructured data in AI?
Examples of unstructured data include social media posts, customer reviews, scanned documents, medical images, and recorded conversations. Monitoring and maintaining the performance of models that handle this data requires advanced tools for ML model monitoring, particularly when dealing with NLP and CV workloads in dynamic environments.