LLM Observability

Fiddler provides a complete workflow to validate, monitor, analyze, and improve prompts and LLMs. 
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Fiddler LLMOps Observability showing a UMAP chart with color-coded clusters
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The New MOOD Stack for LLMOps

The MOOD stack is the new stack for LLMOps to standardize and accelerate LLM application development, deployment, and management. The stack comprises Modeling, AI Observability, Orchestration, and Data layers. 

AI Observability is the most critical layer of the MOOD stack, enabling governance, interpretability, and the monitoring of operational performance and risks of LLMs. This layer provides the visibility and confidence for stakeholders across the enterprise to ensure production LLMs are performant, safe, correct, and trustworthy.

The MOOD stack is the new stack for LLMOps to standardize and accelerate LLM application development, deployment, and management. The stack comprises Modeling, AI Observability, Orchestration, and Data layers.
10 lessons from developing an AI chatbot using RAG
Tips and tricks on managing hallucinations and continuous monitoring for chatbot reliability and trustworthiness.
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Fiddler Solutions for Robust, Correct, Safe, and Secure LLMOps

Enterprises across industries are driving business growth and optimizing productivity by harnessing the power of generative AI. They are launching chatbots and applications powered by LLMs to increase process automation, support customer service and engagement, enhance employee decision making and experience, and more. 

Data Science and Platform Engineering teams can use Fiddler Auditor to evaluate prompts and LLMs for robustness, correctness, and safety and the Fiddler AI Observability platform to:

  • Monitor for hallucination (correctness)
  • PII (privacy and security)
  • Toxicity (safety) metrics
  • Visually analyze trends in prompts and responses and drift
  • Gain insights from dashboards and custom metrics
Pre-production

Fiddler Auditor for LLM and prompt evaluation

Evaluating OpenAI with Fiddler Auditor using prompt evaluation with robustness report.
LLM and Prompt Evaluation

Evaluate the robustness, correctness and safety

Assess LLMs to prevent prompt injection attacks 

Evaluate your LLM and NLP models with Fiddler Auditor
Production

Fiddler AI Observability platform for highly accuracy LLM monitoring and metrics-driven insights

Line chart showing data drift monitoring for OpenAI embeddings
LLM Metrics Monitoring

Get real-time alerts and context on LLM issues

Monitor LLM metrics like toxicity, PII, and hallucinations using Fiddler Trust Service

Fiddler LLMOps Observability showing a UMAP chart with color-coded clusters
Visualization and Insights

Analyze trends in user feedback, safety, and drift via UMAP

Gain insights from dashboards and reports to improve LLMs

Explore how to build generative AI applications for production
The Ultimate Guide to LLM Monitoring
Learn how enterprises should standardize and accelerate LLM application development, deployment, and management
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Increase Oversight on the Quality of LLM Applications

The Fiddler AI Observability platform is designed and built to give enterprises an end-to-end LLMOps experience, from pre-production to production. With Fiddler, you can validate, monitor, analyze, and improve generative AI and LLM applications.

End-to-end LLMOps experience, spanning from pre-production to production
What's New in the Fiddler AI Observability Platform for LLMs
See how Fiddler Trust Models accurately monitor LLM prompts and responses.
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Industry Use Cases for LLMOps

Fiddler supports enterprises across industries to scale their LLM deployments confidently. 

Frequently Asked Questions

What is LLMOps?

Large language model operations (LLMOps) provides a standardized end-to-end workflow for training, tuning, deploying, and monitoring LLMs (open source or proprietary) to accelerate the deployment of generative AI models and applications. 

What is the difference between generative AI and LLM?

Large language models (LLMs) use deep learning algorithms to analyze massive amounts of language data and generate natural, coherent, and contextually appropriate text. Unlike predictive models, LLMs are trained using vast amounts of structured and unstructured data and parameters to generate desired outputs. LLMs are increasingly used in a variety of applications, including virtual assistants, content generation, code building, and more.

Generative AI is the category of artificial intelligence algorithms and models, including LLMs and foundation models, that can generate new content based on a set of structured and unstructured input data or parameters, including images, music, text, code, and more. Generative AI models typically use deep learning techniques to learn patterns and relationships in the input data in order to create new outputs to meet the desired criteria.