LLMOps Observability
An All-in-One Platform for Your LLM Observability and Security Needs
The Fiddler AI Observability and Security platform is built to help enterprises launch accurate, safe, and trustworthy LLM applications. With Fiddler, you can safeguard, monitor, and analyze generative AI (GenAI) and LLM applications in production.
Metrics-driven LLMOps in Production Environments
Proactively Detect LLM Risks
Analyze Issues in Prompts and Responses
Pinpoint High-Density Clusters
Track Key Metrics with AI Observability Dashboards
The MOOD Stack: Empowering AI Observability for LLM Applications
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, accurate, safe, and trustworthy.
Industry Use Cases for LLMOps
Fiddler supports industry leaders to scale and build trust into their LLM deployments.
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.
Guardrails are mechanisms designed to ensure the safe, reliable, and ethical operation of GenAI systems by preventing harmful, unsafe, and unintended outputs and maintaining trust in the GenAI system. It moderates LLM conversations and detects issues like hallucinations, toxic outputs, privacy violations (e.g., PII leakage), or prompt injection attacks in LLM applications, to minimize downstream impact.
