Product
Fiddler AI Observability
Why Fiddler AI Observability
Overview of key capabilities and benefits
LLM Observability
AI Observability for end-to-end LLMOps
Fiddler Trust Service
Guardrails and LLM application monitoring with Fiddler Trust Models
ML Observability
Deliver high performing AI solutions at scale
Model Monitoring
Detect model drift, assess performance and integrity, and set alerts
NLP and CV Monitoring
Monitor and uncover anomalies in unstructured models
Explainable AI
Understand the ‘why’ and ‘how’ behind your models
Analytics
Connect predictions with context to business alignment and value
Responsible AI
Mitigate bias and build a responsible AI culture
See Fiddler in action
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Solutions
Use Cases
Government
Safeguard citizens and national security
AI Governance, Risk Management, and Compliance (GRC)
Enhance AI governance, mitigate risks, and meet compliance standards
Customer Experience
Deliver seamless customer experiences
Lifetime Value
Extend the customer lifetime value
Lending and Trading
Make fair and transparent lending decisions
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
Become a partner
Case Studies
U.S. Navy decreased 97% time needed to update the ATR models
Integral Ad Science scales transparent and compliant AI products with AI Observability
Tide drives innovation, scale, and savings with AI Observability
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Pricing
Pricing Plans
Choose the plan that’s right for you
Plan Comparison
Compare platform capabilities and support across plans
Platform Pricing Methodology
Discover our simple and transparent pricing
FAQs
Pricing answers from frequently asked questions
Build vs Buy
Key considerations for buying AI Observability solution
Contact Sales
Have questions about pricing, plans, or Fiddler?
Resources
Learn
Resource Library
Discover reports, videos, and research
Docs
Get in-depth user guides and technical documentation
Blog
Read product updates, data science research, and company news
AI Forward Summit
Watch recordings on how to operationalize production LLMs, and maximize the value of AI
Connect
Events
Find out about upcoming events
Webinars
Learn from industry experts on pressing issues in MLOps and LLMOps
Contact Us
Get in touch with the Fiddler team
Support
Need help with the platform? Contact our support team
The Ultimate Guide to LLM Monitoring
Learn how enterprises should standardize and accelerate LLM application development, deployment, and management
Read guide
Company
Company
About Us
Our mission and who we are
Customers
Learn how customers use Fiddler
Careers
We're hiring!
Join fiddler to build trustworthy and responsible AI solutions
Newsroom
Explore recent news and press releases
Security
Enterprise-grade security and compliance standards
Featured News
Top 10 AI Companies Shaping the Tech World
Bloomberg: AI-Equipped Underwater Drones Helping US Navy Scan for Threats
AI Observability: The Key to Unlocking the Full Potential of Large Language Models
The insideBIGDATA IMPACT 50 List for Q3 2024
We're on a mission to build trust into AI
Join us
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Run free guardrails
Data Science Blogs
Dive into key concepts, terminology, and cutting-edge research in data science.
Bashir Rastegarpanah and Karen He
Fiddler Report Generator for AI Risk and Governance
Amal Iyer and Karen He
Evaluate LLMs Against Prompt Injection Attacks Using Fiddler Auditor
Murtuza Shergadwala
Making Image Explanations Human-Centric: Decisions Beyond Heatmaps
Josh Rubin
What is ChatGPT Thinking?
Murtuza Shergadwala
Human-Centric Design For Fairness And Explainable AI
Bashir Rastegarpanah
Monitoring Natural Language Processing and Computer Vision Models, Part 3
Ankur Taly
Expect the Unexpected: Why Model Robustness Matters
Amal Iyer
Monitoring Natural Language Processing and Computer Vision Models, Part 2
Bashir Rastegarpanah
Monitoring Natural Language Processing and Computer Vision Models, Part 1
Murtuza Shergadwala
Detecting Intersectional Unfairness in AI: Part 2
Murtuza Shergadwala
Measuring Data Drift with the Population Stability Index (PSI)
Murtuza Shergadwala
Detecting Intersectional Unfairness in AI: Part 1
Avijit Ghosh
Measuring Intersectional Fairness
Malhar Jere
A Practical Guide to Adversarial Robustness
Marissa Gerchick
Identifying Bias When Sensitive Attribute Data is Unavailable: Geolocation in Mortgage Data
Marissa Gerchick
Identifying Bias When Sensitive Attribute Data is Unavailable: Exploring Data From the Hmda
Ankur Taly
[Video] AI Explained: What are Integrated Gradients?
Ankur Taly
AI Explained Video Series: What are Shapley Values?
Josh Rubin
The State of Explainability: Impressions from Partnership on AI (PAI)’s Workshop in NYC
Ankur Taly and Aalok Shanbhag
Counterfactual Explanations vs. Attribution Based Explanations
Marissa Gerchick
Identifying Bias When Sensitive Attribute Data is Unavailable: Techniques for Inferring Protected Characteristics
Marissa Gerchick
Identifying Bias When Sensitive Attribute Data is Unavailable
Ankur Taly
FAccT 2020 - Three Trends in Explainability
Dan Frankowski
Integrated Gradients vs SHAP: How Should You Explain Your Predictions?
Dan Frankowski and Ankur Taly
Causality in Model Explanations and in the Real World
Dan Frankowski
Debugging Predictions Using Explainable AI
Dan Frankowski
A Gentle Introduction to GA2Ms - A White Box Model
Dan Frankowski
Humans Choose - AI Does Not
Dan Frankowski
A Gentle Introduction to Algorithmic Fairness
