Safe and Sound AI

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.

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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. 

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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.

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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. 

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In this episode, we discuss how to monitor the performance of Large Language Models (LLMs) in production environments. We explore common enterprise approaches to LLM deployment and evaluate the importance of monitoring for LLM quality or the quality of LLM responses over time. We discuss strategies for "drift monitoring" — tracking changes in both input prompts and output responses — allowing for proactive troubleshooting and impro...