Track Model Drift on Unstructured Data

Large language models (LLMs) and machine learning (ML) that process unstructured data, such as text, images, and audio, are increasingly central to many business and technology applications. To effectively track how these models change over time, Fiddler leverages embeddings, which are rich vector representations capturing the meaningful features of unstructured inputs.

Learn how these embeddings provide an aggregate drift metric, enable deep insights with clustering, and pinpoint the sources of drift, be it changes in sensors, lighting, or new data introductions. 

This article covers what model drift is, why it matters for unstructured data, and how the Fiddler AI Observability and Security platform helps teams monitor and manage drift effectively.

What is Model Drift?

Model drift occurs when the performance of machine learning models and large language models degrades over time due to changes in the input data or the underlying relationships they have learned. This can happen in two common ways:

• Data drift: When the statistical properties of the incoming data change. For example, new slang in language or different lighting conditions in images.
• Concept drift: When the relationship between inputs and outputs evolves, such as shifts in user behavior or changing product definitions.

Drift presents unique challenges depending on the type of data involved. Models working with structured data, like numbers in a spreadsheet, may face different drift dynamics than models handling unstructured data such as text, images, or audio. For example, a language model (LLM) may struggle as vocabulary and topics evolve, while an image classifier might fail if new types of images or environmental changes occur.

What is Unstructured Data?

Unstructured data is information that does not have a predefined data model or organization. Unlike structured data, which fits neatly into tables and fields, unstructured data includes formats that are more complex and varied, such as:

• Text documents, emails, social media posts, and chat conversations
• Images and video content
• Audio and speech recordings

Because unstructured data is rich and varied, it requires specialized techniques for analysis. This complexity also means that detecting and monitoring model drift for unstructured data is more challenging, as changes in language, visual patterns, or sounds can be subtle and diverse.

Why LLM & ML Model Drift Matters for Unstructured Data

Ignoring model drift in unstructured data can lead to significant problems, including reduced accuracy and reliability of AI predictions. When models degrade, users often experience frustration, which can erode trust in automated systems designed to assist them.

Moreover, businesses risk making incorrect decisions based on faulty model outputs, potentially impacting operations and outcomes. With the growing reliance on AI systems that process unstructured data, continuous monitoring of model drift is essential to maintain model effectiveness and to adapt to ongoing changes in real-world data.

How Monitoring Model Drift Works in Fiddler

Fiddler’s observability platform uses advanced embedding techniques to track drift in unstructured data models efficiently.

Key features include:

• Embedding-based drift metrics: Instead of raw inputs, Fiddler analyzes embeddings (dense vector representations of text or images) that capture deep semantic meaning. This allows for precise drift measurement.
• Aggregate daily metrics: Teams receive a high-level summary of drift to quickly assess overall model health.
• Granular cluster analysis: Data is grouped into clusters or bins, enabling pinpointing of exact sources of drift, such as sensor changes, new image types, or shifts in language use.
• Keyword tagging with TF-IDF: For language models, Fiddler highlights the keywords and phrases contributing most to drift, helping teams understand evolving trends.

These capabilities give data scientists and business users clear, actionable insights to maintain and improve model performance.

How to Monitor Model Drift in Fiddler

Setting up drift monitoring for unstructured data models in Fiddler is straightforward:

1. Ingest raw input-output data: Push your model’s inputs and outputs into Fiddler. There’s no need to manually generate embeddings; Fiddler handles this automatically.
2. Configure drift tracking: Select which drift metrics to monitor and set clustering parameters to define how granular the analysis should be.
3. Use real-time dashboards: Monitor aggregate drift values and detailed cluster-level information through Fiddler’s intuitive dashboards.
4. Set alerts: Define thresholds that trigger notifications when drift exceeds acceptable levels.
5. Investigate causes: Use keyword tags and cluster insights to understand the root causes of drift and prioritize fixes.

Watch the video below for a demonstration of Fiddler’s drift monitoring in action, along with a full transcript for reference.

[INSERT VIDEO AND TRANSCRIPT] 

Protect Your AI Models from Drift

Monitoring LLM and ML model drift is essential to keep your AI systems accurate, reliable, and trustworthy, especially when dealing with complex unstructured data. Without ongoing vigilance, subtle changes in data can lead to degraded performance and unintended consequences.

Fiddler equips your team with the insights needed to detect and address drift early, helping you maintain model quality and confidence over time. Protecting your AI models from drift is about ensuring consistent value and trust for your users and business.

Ready to safeguard your models? Request a demo today to discover how Fiddler can help you stay ahead of drift and keep your AI performing at its best.

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