Build Responsible AI with AI Observability

Fiddler is a pioneer in AI Observability — the foundation you need to scale LLM and ML applications in production. We help instill trust, transparency, and increased oversight in your LLM and ML applications — factors that directly impact your return on investment (ROI). By driving AI governance, risk management, and compliance, Fiddler supports the advancement of responsible AI practices. AI Observability is reliant not only on metrics, but also on how well issues can be explained when something eventually goes wrong. 

Fiddler empowers your Data Science, MLOps, Application Engineering, Risk, Compliance, Analytics, and LOB teams to monitor, analyze, and protect LLM and ML applications.

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Build Trust into AI with Fiddler

• Single Platform for LLM and ML Observability: A unified platform that brings together LLM and ML observability, Fiddler Trust Service, NVIDIA NeMo Guardrails, and contextual model analytics.‍
• Actionable Diagnostics: Deep root cause analysis and actionable model insights for quick issue resolution and model improvement.  ‍
• Built for the Enterprise and Government: Enterprise-grade scalability and stability to support secure LLM and ML deployments in SaaS and VPC environments, including AWS GovCloud for the government.

“One of the things that was appealing to IAS about Fiddler was its ability to customize the monitoring to specific model type, data volume and desired insights. Additionally, the dashboard views, automated alerting and ability to generate audit evidence also factored into the decision to work with Fiddler.”
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— Kevin Alvero, Chief Compliance Officer, Integral Ad Science

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Key Capabilities

Monitoring 

Monitor predictive models and LLMs in pre and post-production and manage all performance metrics at scale in a unified dashboard. From model monitoring alerts to root cause analysis, pinpoint areas of model underperformance and minimize business impact. You can also find quick answers to the root cause and the “why” behind all issues. 

Plug Fiddler into your existing LLM and ML tech stacks for consolidated model monitoring to: 

• Gain efficiencies through faster time-to-market at scale 
• Reduce costs by decreasing errors and the time required to resolve issues 
• Improve collaboration and team alignment with unified monitoring and silo elimination 

Analytics 

Analytics must deliver actionable insights that power data-driven decisions. To improve predictions, market context and business alignment must be baked into modeling so results reflect the needs and challenges of your business. 

Use descriptive and prescriptive analytics from ML models and LLMs to make decisions so you can: 

• Deploy higher ROI models to increase revenue 
• Align decisions to stay in lockstep with business needs 
• Respond quickly and refine models when market dynamics shift

Fairness 

Responsible AI is the practice of building transparent, accountable, ethical, and reliable AI. The first step is detection and mitigation of bias in tabular and unstructured datasets and LLM/ML models, but you must also support internal governance processes and reduce risk through human involvement. 

Build and deploy responsible AI solutions with bias detection and fairness assessment in order to: 

• Reduce risk by instilling trust with continuous AI monitoring and human decision-making with LLM and ML 
• Provide visibility and governance to internal oversight teams
• Mitigate model bias through the detection, comparison, and measurement of dataset bias

LLM Use Cases

• AI Chatbot Monitoring
• Content Summarization
• LLM Cost Management
• Internal Copilot Monitoring
• AI Governance
• Risk Management
• Compliance

ML Use Cases

• Anti-money Laundering
• Fraud Detection
• Credit Scoring Investment
• Decision-Making
• Underwriting
• Churn Detection
• Risk Management
• Compliance