Author name: Fiddler AI Blog

Discover in our blog how to ensure successful LLM deployments with comprehensive AI Observability, and monitor LLMs for performance, safety, privacy, and correctness.

Hallucinations in LLMs pose risks and harm to enterprises and consumers. Read the blog on detecting hallucinations using LLM metrics to improve LLM performance.

The LLMOps stack for LLM-powered apps comprises a “MOOD” framework of Models, Observability, Orchestration, and Data, enabling seamless integration and comprehensive oversight.

Key insights on AI safety and alignment, featuring scalable oversight, generalization, robustness, interpretability, governance, and the journey towards aligning AI with human values.

Decide whether to build or buy Fiddler AI observability tools to scale MLOps, support LLMOps, and drive responsible, enterprise-ready AI.

Compare metrics and inferences for AI observability to help enterprises monitor models effectively with Fiddler’s AI observability platform.

Fiddler’s patented clustering-based drift monitoring method detects subtle behavior changes in text, computer vision, and LLM-based models.

Adopt an AI safety culture across the enterprise by adopting governance and responsible AI frameworks to manage risks of generative AI.

The Fiddler and Domino integration helps companies accelerate the production of AI solutions and streamline their end-to-end MLOps and LLMOps observability workflows.

DataStax and Fiddler empower AI teams to deliver scalable, responsible, and helpful RAG-based AI Applications.