ML Model Monitoring Resources

Detect model drift, assess performance and integrity, and set alerts

Resources

• AI Forward Summit
• Generative AI and LLMOps
• Use Cases
• Responsible AI
• AI Observability
• MLOps
• Bias and Fairness
• Explainable AI

Video

Compare and Analyze Cohorts Using Segment Monitoring

In this product tour, see how to configure Fiddler’s dashboards for specific teams, business goals, and use cases.

Get Rich Insights on LLM Applications and ML Models with Customizable Dashboards

In this product tour, see how to configure Fiddler’s dashboards for specific teams, business goals, and use cases.

Track Model Drift on Unstructured Data

In this product tour, we walk through tracking drift for models like image classifiers by leveraging image embeddings and text embeddings.

Measuring Distributional Shifts in Text: The Advantage of Language Model-Based Embeddings

Explore a method for measuring distributional shifts in text data using language model-based embeddings, highlighting the effectiveness of LLMs in capturing semantic relationships for this purpose.

Quicktour: Monitor NLP and LLM-based Embeddings for Data Drift

Learn how Fiddler’s unique, clustering-based method accurately monitors data drift in NLP models and LLM-based embeddings.

LLM-based Embedding Monitoring: Track LLM Performance by Monitoring Drift

Learn how to track the performance of LLM-based embeddings from OpenAI, Cohere, Anthropic, and other LLMs by monitoring drift using Fiddler.

Custom Dashboards and Insights: Increase Organizational Alignment for Better Decision Making

Learn how Fiddler’s custom dashboards help ML teams obtain actionable model insights, and increase organizational alignment and collaboration.

Monitor Model Performance and Drift

Learn how to quickly detect model performance and drift issues, and reduce the time to troubleshoot issues with root cause analysis using Fiddler.

Monitoring Models with Unstructured Data

Learn how to monitor models with unstructured data using Fiddler's cluster-based binning approach.

A Human-Centric Take on Model Monitoring

Learn the human-centric challenges and requirements for ML model monitoring in real-world applications.

AI Explained: Unraveling Unstructured Text and Image Models

Watch this on-demand webinar with Josh Rubin, Director of Data Science at Fiddler AI, to understand how to improve unstructured model performance.

The New Fiddler for Unstructured Models and Advanced XAI

Watch this demo-driven webinar to learn the major updates to the Fiddler MPM platform.

AI Explained: Rethinking Model Monitoring

Watch this on-demand AI Explained with Shreya Shankar, PhD Student at UC Berkeley, to learn the mess plaguing ML workflows, emerging research around model monitoring, and how to build responsible AI.

AI Explained: Model Monitoring Best Practices IRL

Watch this on-demand webinar with Hima Lakkaraju, Assistant Professor at Harvard University, to learn model monitoring best practices, why an organization should have it, and how to integrate it into MLOps workflows.

Guides

• The Ultimate Guide to Large Language Model (LLM) Monitoring
  Learn how enterprises use large language model (LLM) Monitoring using a comprehensive AI Observability platform to ensure high performance, behavior, and safety of LLM Applications.
• The Rise of MLOps Monitoring
  Read about the rise of MLOps monitoring and how it helps IT teams accelerate the life cycle of development. Prepare for a successful AI deployment today.
• Model Monitoring Best Practices
  Download the whitepaper to learn best practices for model monitoring, model monitoring tools and techniques, and the role of explainability.
• Class Imbalance: Monitoring Rare and Nuanced Model Drift
  Download the whitepaper to learn what class imbalance is, how to detect drift, the impact on ML models, and how to address it for effective model monitoring.
• How to Measure ML Model Drift
  Download the whitepaper to learn why model drift is important, how to measure model drift, and more.
• Model Performance Management Best Practices
  Learn the unique nature of machine learning, its challenges, and how to create a disciplined model performance management framework.
• The Ultimate Guide to Model Performance
  ML models naturally degrade in performance over time. To catch and correct performance issues, teams must monitor model performance throughout the ML lifecycle.

Articles

• Agentic Framework in AI: A Comprehensive Analysis
  Explore the agentic framework in AI, its differences from other frameworks, and its applications in autonomous development.
• Agentic AI in Healthcare: Driving Smarter Clinical Decisions and Patient Engagement
  Explore how agentic AI is transforming healthcare by enabling smarter clinical decisions and improving patient engagement.
• Agentic AIOps: The Evolution of AI Agents for Next-Gen Operations with Agentic AI Platforms
  Discover how Fiddler Agentic AIOps transforms AI operations, enhancing decision-making and optimizing workflows for next-gen AI systems.
• Leveraging AI Guardrail Metrics to Strengthen LLM Reports
  Explore how AI guardrails reports and LLM metrics enhance enterprise AI security, accountability, and performance with Fiddler Trust Service.
• Why AI Security is Critical for Enterprise Organizations
  Learn why AI security is essential for enterprises and how platforms like Fiddler help protect models, data, and systems at scale.
• A Complete Guide to Machine Learning Model Lifecycle Management
  Learn how to effectively manage the machine learning model lifecycle and discover how Fiddler streamlines model monitoring for enterprise success.
• Evaluating the ROI of AI Explainability Tools
  Discover how Fiddler AI’s explainability improves ML performance, transparency, and trust. Gain clearer insights into ML model predictions and outcomes.
• Navigating AI Compliance and Risk Management
  Discover how the Fiddler AI Observability and Security platform helps organizations manage LLM compliance and risks, ensuring safe and trustworthy LLM deployment at scale.
• Maximizing AI Guardrails Velocity: Fast, Secure Innovation at Scale
  Discover how AI Guardrails accelerate innovation without sacrificing security. Learn how Fiddler enables fast, secure LLM deployment at scale.
• How to Avoid LLM Security Risks
  Discover how to avoid LLM security risks with Fiddler AI. Our observability and guardrails tools detect risks and set alerts for quick jailbreak responses.
• AI Agent Monitoring: Key Steps and Methods to Ensure Performance and Reliability
  Learn how to evaluate AI agents with key steps and metrics using Fiddler to boost performance, ensure reliability, and support continuous improvement.
• Guide to Building RAG-Based LLM Applications for Production
  Discover how to build RAG-based LLM applications for production with Fiddler, and learn how to safeguard, monitor, and optimize LLM performance.
• How to Leverage AI Predictive Analytics to Forecast Model Behavior
  Discover how AI predictive analytics helps forecast model behavior, optimize outcomes, and improve decision-making with Fiddler AI Observability.
• Managing Responsible Multi-Agent LLM Systems for Enterprise Applications
  Learn best practices, importance, and benefits of managing multi-agent LLM systems in enterprises. Discover how Fiddler can assist with seamless management.
• How to Build an AI Agent: A Step-by-Step Guide for Enterprises
  Learn how to build AI agents with this step-by-step guide for enterprises—covering frameworks, benefits, use cases, and Fiddler’s AI monitoring support.