Safe and Sound AI

• Agentic Observability: The AI Architect's Essential Blueprint

  In this episode of Safe and Sound AI, we dive into the challenge of moving AI agents from impressive demos to robust, production-ready systems. We break down the principles of Agentic Observability, explaining how this essential "blueprint" provides the clarity needed to overcome the "black box" problem during both development and production.  Learn practical methods for monitoring key signals like tool usage and planning, discover how to diagnose the root causes of agent failures, and explore strategies for ensuring your agent delivers real-world value.

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  13:39

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  13:39
• How to Identify ML Drift Before You Have a Problem

  In this episode of Safe and Sound AI, we dive into the challenge of drift in machine learning models. We break down the key differences between concept and data drift (including feature and label drift), explaining how each affects ML model performance over time. Learn practical detection methods using statistical tools, discover how to identify root causes, and explore strategies for maintaining model accuracy. Read the article by Fiddler AI and explore additional resources on how AI Observability can help build trust into LLMs and ML models.

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  9:14

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  9:14
• Industry’s Fastest Guardrails Now Native to NVIDIA NeMo

  In this episode, we discuss the new integration between Fiddler Guardrails with NVIDIA Nemo Guardrails, pairing the industry's fastest guardrails with your secure environment. We explore the setup process, practical implications, and the role of the Fiddler Trust Service in providing guardrails, monitoring and custom metrics. Plus, we highlight the free trial opportunity to experience Fiddler Guardrails firsthand.  Read the article to learn more, or sign up for the Fiddler Guardrails free trial to test the integration for yourself.

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  10:14

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  10:14
• Introducing Fiddler Guardrails: The Fastest in the Industry

  In this episode, we explore how Fiddler Guardrails helps organizations keep large language models (LLMs) on track by moderating prompts and responses before they can cause damage. We break down its industry best latency, secure deployment options, and how it works with Fiddler’s AI observability platform to provide the visibility and control to adapt to evolving threats. Read the article to learn more about how Fiddler Guardrails can help safeguard your LLM Applications.

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  7:20

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  7:20
• Should you Observe ML Metrics or Inferences?

  In this episode, we explore two key approaches for monitoring AI models: metrics and inference observation. We break down their trade-offs and provide real-world examples from various industries to illustrate the advantages of each model monitoring strategy for driving responsible AI development.  Read the article by Fiddler AI and explore additional resources for more information on how AI observability can help developers build trust into AI services.

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  12:51

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  12:51

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