LLMOps Observability
Fiddler is Your Insurance Policy
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.
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
Fiddler Auditor for LLM and prompt evaluation
Evaluate the robustness, correctness and safety
Assess LLMs to prevent prompt injection attacks
Fiddler AI Observability platform for continuous monitoring and metrics-driven insights
Get real-time alerts and context on LLM issues
Continuously monitor LLM metrics like toxicity, PII, and hallucinations
Analyze trends in user feedback, safety, and drift via UMAP
Gain insights from dashboards and reports to improve LLMs
A Continuous Feedback Loop for LLMOps
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 protect generative AI and LLM applications.
Metrics-driven LLMOps for Developers, Platform Engineering, and Business Teams
Industry Use Cases for LLMOps
Fiddler supports enterprises across industries to scale their LLM deployments confidently.
Featured Resources
Frequently Asked Questions
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.
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.