Product
Platform Capabilities
Why Fiddler AI Observability
Key capabilities and benefits
Explainable AI
Understand the ‘why’ and ‘how’ behind your models
NLP and CV Models
Monitor and uncover anomalies in unstructured models
LLMOps
AI Observability for end-to-end LLMOps
Security
Enterprise-grade security and compliance standards
MLOps
Deliver high performing AI solutions at scale
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Model Monitoring
Detect model drift, assess performance and integrity, and set alerts
Analytics
Connect predictions with context to business alignment and value
Fairness
Mitigate bias and build a responsible AI culture
Improve your AI models. Request a demo
Solutions
Customer Experience
Deliver seamless customer experiences
Lending and Trading
Make fair and transparent lending decisions with confidence
Case Studies
how.fm reduces time to detect model issues from days to minutes
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Conjura reduces time to detect and resolve model drift from days to hours
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Lifetime Value
Extend the customer lifetime value
Risk and Compliance
Minimize risk with model governance and ML compliance
Government
Safeguarding citizens and national security with trusted AI
Pricing
Pricing Plans
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Platform Pricing Methodology
Discover our simple and transparent pricing
Plan Comparison
Compare platform capabilities and support across plans
FAQs
Obtain pricing answers from frequently asked questions
Build vs Buy
Key considerations for buying an AI Observability solution
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Resources
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Featured Resources
Introducing Fiddler Auditor
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Operationalize Models at Scale
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Model Monitoring Best Practices
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Resource Library
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Amazon SageMaker + Fiddler
End-to-end model lifecycle management
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Join Fiddler AI to build trustworthy and responsible AI solutions
Featured news
Fiddler AI is on a16z's inaugural Data50 list of the world's top 50 data startups
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Bias and fairness in AI blogs
Learn how to create fair, ethical AI and the key challenges ML teams face when addressing fairness and model bias.
Murtuza Shergadwala
Human-Centric Design For Fairness And Explainable AI
Amit Paka
FairCanary: Rapid Continuous Explainable Fairness
Murtuza Shergadwala
Detecting Intersectional Unfairness in AI: Part 2
Krishna Gade
AI Regulations Are Here. Are You Ready?
Murtuza Shergadwala
Detecting Intersectional Unfairness in AI: Part 1
Amit Paka
Introducing Bias Detector: A New Methodology to Assess Machine Learning Fairness
Anusha Sethuraman
Responsible AI Podcast with Anand Rao – “It’s the Right Thing to Do”
Avijit Ghosh
Measuring Intersectional Fairness
Mary Reagan
Understanding Bias and Fairness in AI Systems
Anusha Sethuraman
AI in Finance Panel: Accelerating AI Risk Mitigation with XAI and Continuous Monitoring
Anusha Sethuraman
How Do We Build Responsible, Ethical AI?
Amit Paka
How to Build a Fair AI System
Krishna Gade
TikTok and the Risks of Black Box Algorithms
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
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
Krishna Gade
The Never-ending Issues Around AI and Bias – Who’s to Blame When AI Goes Wrong?
Amit Paka
Regulations To Trust AI Are Here. And It's a Good Thing
Kent Twardock
Can Congress Help Keep AI Fair for Consumers?
Dan Frankowski
A Gentle Introduction to Algorithmic Fairness
