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Product
Control Plane for Agents
Systems of record for the agentic lifecycle
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Why Fiddler AI Observability
Test, observe, protect, and govern AI at enterprise scale
Agentic Observability
End-to-end visibility, context, and control for the agentic lifecycle
Fiddler Trust Service
Purpose-built trust models for secure, in-environment evaluation and guardrails
Guardrails
Protect agentic applications with the industry's fastest guardrails
AI Governance, Risk Management, and Compliance
Centralized control and accountability for enterprise AI governance and compliance
Responsible AI
Mitigate bias and build a responsible AI culture
ML Observability
Deliver high performing AI solutions at scale
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Solutions
Industry
Government
Mission-critical AI for defense and intelligence operations
Healthcare
Deploy agents for clinical care and patient outcomes safely
Insurance
Scale trusted agents across insurance claims, underwriting, and risk assessment
Use Cases
Customer Experience
Deliver agentic experiences that delight customers
Lifetime Value
Maximize customer lifetime value with agentic AI
Lending and Trading
Run autonomous financial AI operations at scale
Partners
Amazon SageMaker AI
Unified MLOps for scalable model lifecycle management
Google Cloud
Deploy safe and trustworthy AI applications on Vertex AI
NVIDIA NIM and NeMo Guardrails
Monitor and protect LLM applications
Databricks
Accelerate production ML with a streamlined MLOps experience
Datadog
Gain complete visibility into the performance of your AI applications
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Case Studies
Nielsen Operationalizes Trust for a Production Multi-Agent AI Copilot
U.S. Navy decreased 97% time needed to update the ATR models
Integral Ad Science scales transparent and compliant AI products with AI Observability
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Read product updates, data science research, and company news
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Watch recordings on how to operationalize production LLMs, and maximize the value of AI
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Events
Find out about upcoming events
Webinars
Learn from industry experts on pressing issues facing Agentic and ML teams.
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Get in touch with the Fiddler team
2025 Enterprise Guardrails Benchmarks Report
Which guardrails solution is right for your organization? One size never fits all — and the stakes couldn't be higher.
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Company
Company
About Us
Our mission and who we are
Customers
Learn how customers use Fiddler
Careers
We're hiring!
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Newsroom
Explore recent news and press releases
Security
Enterprise-grade security and compliance standards
Featured News
AP News: Fiddler Raises $30M Series C to Power the Control Plane for AI Agents
WSJ Venture Capital: The $1 Trillion Hope Building Around Artificial Intelligence
CB Insights: AI Agents Need Security
Bloomberg: AI-Equipped Underwater Drones Helping US Navy Scan for Threats
We're on a mission to build trust into AI
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Run free guardrails
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Blog
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Model Monitoring
Model Monitoring Blogs
Learn why model monitoring is a critical part of the MLOps and LLMOps lifecycle in both pre and post-production for all types of models.
July 2, 2025
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Segio Ferragut, Karen He, Danny Brock
Proactive Drift and Data Quality Monitoring for Tecton Feature Views with Fiddler
MLOps
Model Monitoring
May 8, 2025
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Segio Ferragut, Karen He, Danny Brock
Preventing Model Decay: Tecton + Fiddler for ML Drift Detection
MLOps
Model Monitoring
Explainable AI
May 7, 2025
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Cole Martin
Fiddler is Selected for DoD’s APFIT Award to Accelerate Mission-Critical AI
MLOps
Model Monitoring
Explainable AI
March 4, 2024
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Danny Brock and Karen He
AI Observability: The Build vs. Buy Dilemma
Model Monitoring
MLOps
Generative AI and LLMOps
February 15, 2024
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Danny Brock and Karen He
Choosing Between Metrics and Inferences for Model Monitoring
Model Monitoring
MLOps
Generative AI and LLMOps
February 12, 2024
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Amal Iyer and Barun Halder
The Advantage of Language Model-Based Embeddings
Model Monitoring
August 15, 2023
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Karen He and Anushrav Vatsa
Accelerating the Production of AI Solutions with Fiddler and Databricks Integration
Company
MLOps
Model Monitoring
June 6, 2023
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Karen He
91% of ML Models Degrade Over Time
MLOps
Model Monitoring
April 4, 2023
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Amit Paka, Krishna Gade, and Krishnaram Kenthapadi
The Missing Link in Generative AI
Model Monitoring
Generative AI and LLMOps
February 6, 2023
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Bashir Rastegarpanah
Monitoring Natural Language Processing and Computer Vision Models, Part 3
Model Monitoring
Data Science
December 15, 2022
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Amal Iyer
Monitoring Natural Language Processing and Computer Vision Models, Part 2
Model Monitoring
Data Science
December 8, 2022
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Shohil Kothari
5 Things to Know About ML Model Performance
Model Monitoring
October 3, 2022
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Bashir Rastegarpanah
Monitoring Natural Language Processing and Computer Vision Models, Part 1
Model Monitoring
Data Science
September 30, 2022
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Shohil Kothari
ML Model Monitoring Best Practices
Model Monitoring
August 29, 2022
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Shohil Kothari
Top 4 Model Drift Metrics
Model Monitoring
August 24, 2022
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Shohil Kothari
What is Class Imbalance?
Model Monitoring
April 19, 2022
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Shohil Kothari
Implementing Model Performance Management in Practice
Model Monitoring
Explainable AI
February 9, 2022
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Amy Holder
Q&A with Bigabid CTO: Monitoring Thousands of Models in Production
Use Case
Model Monitoring
January 31, 2022
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Amy Holder
Drift in Machine Learning: How to Identify Issues Before You Have a Problem
Model Monitoring
MLOps
April 1, 2021
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Amit Paka
Why Data Integrity is Key to ML Monitoring
Model Monitoring
MLOps
January 20, 2021
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Anusha Sethuraman
Explainable Monitoring for Successful Impact with AI Deployments
Explainable AI
Model Monitoring
November 17, 2020
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Amit Paka
AI in Banking: Rise of the AI Validator
Explainable AI
Model Monitoring
September 14, 2020
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Amit Paka
The Rise of ML Monitoring
Model Monitoring
MLOps
September 3, 2020
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Erika Renson
AI Explained Video Series: The AI Concepts You Need to Understand
Explainable AI
Model Monitoring
August 20, 2020
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Amit Paka
How to Detect Model Drift in ML Monitoring
Model Monitoring
MLOps
June 1, 2020
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Amit Paka
Enterprise Monitoring Landscape - Overview and New Entrants
Model Monitoring
April 20, 2020
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Anusha Sethuraman
Webinar: Why Monitoring is Critical to Successful AI Deployments
Model Monitoring
