Stacey Johnston, MD, Chief Information and Digital Execution Officer at Beacon Health System, shares how practicing medicine shapes AI governance, from structured approval processes to ROI mandates and monitoring requirements before any tool scales.

Jeff Dalton, Head of AI and Chief Scientist at Valence, joins us to discuss why enterprises are still thinking in models when they should be thinking in systems, and how to close the agentic AI gap.

David Kenny, Executive Chairman of the Board at Nielsen, shares insights on what it means for the enterprise when AI agents take over decision-making, and what it takes to maintain control.

Watch this demo-drive webinar to learn how Fiddler delivers end-to-end agentic observability so enterprises can deploy high-performing agents at scale with visibility, context, and control.

Gary Stafford, Principal Solutions Architect at AWS, shares insights from his hands-on work with AWS Strands Agents, exploring approaches enterprises can take to design agents that are flexible, observable, and modular enough to scale and evolve over time.

Jayeeta Putatunda, Director of AI Center of Excellence at Fitch Group, explores the real-world challenges of deploying agentic systems, why testing falls short, and how to use continuous observability to diagnose failures and build trust in AI agents.

Nate B. Jones, Head of Product at Rockerbox, explores the current state of AI agents with strategist as he breaks down adoption gaps, architectural trade-offs, and what it takes to move from prototype to production.

Watch this demo-driven webinar to see how Fiddler protects LLMs with AI guardrails, monitors 50+ LLM metrics and 30 ML metrics, and natively integrates with Amazon SageMaker AI and NVIDIA NeMo Guardrails.

Karthik Bharathy, General Manager, AI Ops & Governance for Amazon SageMaker AI at AWS, discusses AI agent visibility, mitigating security risks with Guardrails, and compliance in multi-agent systems.

Dr. Girish Nadkarni, Icahn School of Medicine at Mount Sinai, discusses his experience with GenAI in healthcare including use cases, key risks, and best practices.

Navrina Singh, Founder and CEO at Credo AI, discusses meeting GRC standards, understanding generative AI risks, and creating adaptive compliance strategies.

Jonathan Cohen, VP of Applied Research at NVIDIA, discusses the future of AI agents, ethical considerations, and the evolving landscape of LLM monitoring.

Watch the recording to hear Kevin Schawinski, CEO and Co-Founder at Modulos AG, discuss the EU AI Act’s requirements and give insights on compliance, ethical AI, and staying competitive in a regulated market.

Watch Robert Nishihara, Co-founder and CEO at Anyscale, discuss reliability challenges, building the right AI infrastructure, and implementing the latest practices in productionizing GenAI at scale.

Watch this demo-driven webinar where we will show major product enhancements to the Fiddler AI Observability Platform for predictive and generative models. See Fiddler LLM Enrichments Framework and advanced image explainability in action.

Pradeep Javangula, Chief AI Officer at RagaAI, discusses what metrics to measure the performance of LLMs, approaches to evaluating and monitoring hallucination metrics, and tips for testing LLM applications.

Join this AI Explained to explore AI safety, focusing on scalable oversight, robustness, and interpretability. Learn how to align AI with human values for a safer future.

Watch AI Explained to hear Kathy Baxter, Principal Architect of Responsible AI & Tech at Salesforce, highlight the multifaceted nature of managing risks in generative AI, underscoring the need for accuracy, safety, and transparency.

Watch this demo-driven webinar where we will show major product upgrades to the Fiddler AI Observability Platform for predictive and generative models.

Watch Chaoyu Yang, Founder and CEO at BentoML, discuss the challenges in moving LLM app prototypes into production, when to consider using an open-source LLM vs OpenAI endpoints, and more.

Watch our discussion with Jure Leskovec, Professor of Computer Science at Stanford University and Co-founder at Kumo.AI, on considerations of incorporating GNNs with generative AI models and LLM workflows, and examples of real-world AI applications using GNNs

Watch Parul Pandey, Principal Data Scientist at H2O.ai, discuss how data scientists and ML practitioners can improve AI outcomes in production with proper model risk management techniques.

Watch this latest on-demand webinar with Peter Norvig, Distinguished Education Fellow at Stanford’s Human-Centered Artificial Intelligence Institute, to learn human-in-the-loop best practices for generative applications, considerations for AI safety, and more!

Watch this demo-driven webinar to learn how to monitor OpenAI embeddings and evaluate the robustness of LLMs and NLP models.

Watch our exclusive webinar with Patrick Hall, Co-Founder of BNH.AI and visiting Faculty at The George Washington School of Business, to learn about AI risk management, model governance, and the intersection of AI, law, and ethics.

Watch this AI Explained to hear from Alex Ratner, Co-founder and CEO at Snorkel AI, on challenges associated with building Generative AI based applications and more!

Watch this AI Explained to hear from Dilek Hakkani-Tür, Senior Principal Scientist at Amazon Alexa AI, on what you need to know about conversational AI.

Listen to Goku Mohandas, Founder at Made with ML, discuss the comprehensive requirements for the end-to-end MLOps lifecycle and how to prove ROI for your ML initiatives.

Watch this on-demand webinar to learn from Tirthajyoti Sarkar, Sr. Director, AI/ML Platform at Rhombus Power Inc, on the value of XAI for ML models, how to do XAI right, and why explainability is critical.

Watch this on-demand webinar with Merve Hickok, Founder at AIethicist.org, to understand the White House's recently released AI Bill of Rights.

Watch this on-demand webinar with Josh Rubin, Director of Data Science at Fiddler AI, to understand how to improve unstructured model performance.

Watch this demo-driven webinar to learn the major updates to the Fiddler MPM platform.

Watch this on-demand AI Explained with Shreya Shankar, PhD Student at UC Berkeley, to learn the mess plaguing ML workflows, emerging research around model monitoring, and how to build responsible AI.

Watch this on-demand webinar with Hima Lakkaraju, Assistant Professor at Harvard University, to learn model monitoring best practices, why an organization should have it, and how to integrate it into MLOps workflows.

Learn how insurance carriers can monitor AI agents, address NAIC mandates, and automate compliance for predictive, generative, and agentic AI systems.

Download the AI Observability checklist for insurers to build a compliant AIS program, mitigate financial risk, and ensure fair, auditable AI in line with NAIC mandates.

Build production-ready AI agents that scale. Go beyond the prototype with 5 field-tested lessons on observability, non-deterministic testing, and architecture.

Unlock safe, high-performing AI agents in healthcare. Learn observability tactics, a clinical scribe case study, and a unified framework for ML, LLMs & agents.

Download Fiddler’s AI Observability checklist for healthcare enterprises to reduce risk, protect patient data, and ensure compliant, trustworthy AI from setup to monitoring.

Discover why aggregated, context-rich observability is key to scaling trust. Traditional monitoring can’t keep up with AI agents.

Discover the Four Levels of Responsible AI Maturity in our technical brief. Gain valuable insights into integrating AI, measuring business outcomes, and tracking ML and LLM metrics.

Fiddler's AI Observability platform supports responsible AI by monitoring bias and fairness, and ensuring compliance with AI GRC standards. Read guide.

Learn how enterprises use large language model (LLM) Monitoring using a comprehensive AI Observability platform to ensure high performance, behavior, and safety of LLM Applications.

Machine learning operations (MLOps) is about tradeoffs, context, and building an AI-first culture. Learn best practices and principles to help you get started.

Download the whitepaper to learn best practices for model monitoring, model monitoring tools and techniques, and the role of explainability.

ML models naturally degrade in performance over time. To catch and correct performance issues, teams must monitor model performance throughout the ML lifecycle.

Download the brief to learn how explainable AI works in Fiddler, proprietary techniques used to explain model behavior, and Explainable AI use cases

Download the whitepaper to learn what class imbalance is, how to detect drift, the impact on ML models, and how to address it for effective model monitoring.

Download the whitepaper to learn why model drift is important, how to measure model drift, and more.

Learn the unique nature of machine learning, its challenges, and how to create a disciplined model performance management framework.

Read about the rise of MLOps monitoring and how it helps IT teams accelerate the life cycle of development. Prepare for a successful AI deployment today.

Learn how to build ethical AI using explainable AI in these whitepapers. If you use artificial intelligence you need to ensure it's responsible and fair.

Learn how a leading healthcare payer cut GenAI compliance costs by 75% while meeting strict HIPAA, CMS, and NIST requirements with Fiddler Guardrails.

Learn how a leading healthcare enterprise centralized governance for 60+ models on Google Cloud, using Fiddler to prevent PHI leakage and accelerate innovation.

Learn how a leading insurance carrier uses Fiddler to govern agentic AI in claims operations, maintain NAIC compliance, and generate audit evidence for responsible AI.

Learn how a leading insurance carrier uses Fiddler to govern agentic AI in claims operations, maintain NAIC compliance, and generate audit evidence for responsible AI.

Nielsen operationalizes trust at scale with Fiddler AI, blocking 100% toxic content and jailbreaks at 99% precision across a production multi-agent AI copilot.

Discover how the U.S. Navy reduced ML model retraining time by 97% with Fiddler AI Observability, enhancing accuracy, transparency, and operational readiness.

Discover how a leading gig marketplace uses Fiddler for AI monitoring, optimizing LLMs and ML models, boosting data security, and enhancing customer service.

Discover how a global travel marketplace improved AI trust and transparency while optimizing ML models with Fiddler’s AI Observability Platform.

Discover how a top consumer lending platform uses Fiddler AI Observability to enhance ML monitoring, improve explainability, and save over 2,000 hours annually.

Fiddler helped Integral Ad Science rapidly launch and scale AI, align with the highest standards of quality and transparency, and adhere to AI regulations.

On this episode, we are joined by Stacey Johnston, MD, CIO at Beacon Health System. She shares how agentic AI is delivering ROI and why clinician trust starts with workflow fit.

On this episode, we are joined by Jeff Dalton, Head of AI and Chief Scientist at Valence. He shares why enterprises should be thinking in systems and how to close the agentic gap.

On this episode, we are joined by David Kenny, Executive Chairman of the Board of Nielsen and veteran AI leader. He shares practical insights on preventing AI agents from going rogue and building trustworthy agentic systems.

Learn how Agentic Observability delivers the trust and control needed to manage complex multi-agent AI systems where traditional monitoring fails.

On this episode, we are joined by Gary Stafford, Principal Solutions Architect at AWS Strands Agents. He explores agentic approaches, multi-agent system patterns, and MCP’s role in enterprise AI adoption.

On this episode, we are joined by Jayeeta Putatunda, Director of AI Center of Excellence at Fitch Group. She shares practical insights on building and deploying AI agent systems.

Learn the essentials of agentic observability. We cover how to monitor AI agent behavior, diagnose failures & build reliable, production-ready AI.

On this episode, we are joined by Nate B. Jones, AI strategist to discuss challenges in building AI agents, architectural decisions, and ensuring ROI from these agents.

In this episode, we explain model drift in Machine learning — covering concept vs. data drift, detection methods, and practical solutions for maintaining AI performance.

In this episode, we discuss how Fiddler Guardrails integrates natively with NVIDIA Nemo Guardrails to deliver faster, safer, and more reliable LLM applications.

On this episode, we are joined by Karthik Bharathy, General Manager, AI Ops & Governance for Amazon SageMaker AI at AWS to discuss agentic AI systems, and more.

In this episode, we’re joined by Dr. Girish Nadkarni from Icahn School of Medicine at Mount Sinai to discuss AI in healthcare, key risks, and future of AI in medicine.

In this episode, we discuss Fiddler Guardrails, a powerful new tool that proactively moderates risky LLM prompts and responses before they can cause damage.

In this episode, we discuss the methods, advantages, tradeoffs of monitoring ML metrics versus ML inferences using AI Observability.

In this episode, we discuss how to monitor the performance of LLMs in production environments and explore common enterprise approaches to LLM deployment, and more.

In this episode, we’re joined by Navrina Singh from Credo AI to explore the future of AI compliance, GRC for GenAI, and implementing responsible AI practices.

In this episode, we’re joined by Jonathan Cohen from NVIDIA. He will discuss tools for AI model deployment, security, and observability, along with the evolving role of AI agents and real-world enterprise applications.

On this episode, we’re joined by Kevin , CEO and Co-Founder at Modulos AG. Schawinski will discuss the EU AI Act’s requirements, implementing responsible AI, and more.

In this episode, we’re joined by Robert Nishihara, Co-founder and CEO at Anyscale. Nishihara will discuss reliability challenges affecting GenAI production environments and more.

In this episode, we’re joined by Pradeep Javangula, Chief AI Officer at RagaAI. Javangula will discuss what hallucinations are, metrics to measure the performance of LLMs, and more.

In this episode, we’re joined by Amal Iyer, Sr. Staff AI Scientist at Fiddler AI. Iyer will explore AI safety, focusing on scalable oversight, robustness, and interpretability.

On this episode, we’re joined by Kathy Baxter, Principal Architect of Responsible AI & Tech at Salesforce. Baxter will discuss ethical AI practices organizations can follow.

On this episode, we’re joined by Patrick Hall, Co-Founder of BNH.AI. Hall will discuss AI risk management, model governance, and the intersection of AI, law, and ethics.

On this episode, we’re joined by Chaoyu Yang, Founder and CEO at BentoML. Yang will explore key aspects of generative AI application development and deployment.

On this episode, we’re joined by Jure Leskovec, Stanford professor and co-founder at Kumo.ai. Leskovec pioneered the field of Graph Neural Networks and co-authored PyG.

On this episode, we’re joined by Parul Pandey, Principal Data Scientist at H2O.ai and co-author of Machine Learning for High-Risk Applications.

On this episode, we’re joined by Peter Norvig, a Distinguished Education Fellow at the Stanford Institute for Human-Centered AI and co-author of popular books on AI, including Artificial Intelligence: A Modern Approach and more recently, Data Science in Context.

Explore the agentic framework in AI, its differences from other frameworks, and its applications in autonomous development.

Explore how agentic AI is transforming healthcare by enabling smarter clinical decisions and improving patient engagement.

Discover how Fiddler Agentic AIOps transforms AI operations, enhancing decision-making and optimizing workflows for next-gen AI systems.

Explore how AI guardrails reports and LLM metrics enhance enterprise AI security, accountability, and performance with Fiddler Trust Service.

Learn why AI security is essential for enterprises and how platforms like Fiddler help protect models, data, and systems at scale.

Learn how to effectively manage the machine learning model lifecycle and discover how Fiddler streamlines model monitoring for enterprise success.

Discover how Fiddler AI’s explainability improves ML performance, transparency, and trust. Gain clearer insights into ML model predictions and outcomes.

Discover how the Fiddler AI Observability and Security platform helps organizations manage LLM compliance and risks, ensuring safe and trustworthy LLM deployment at scale.

Discover how AI Guardrails accelerate innovation without sacrificing security. Learn how Fiddler enables fast, secure LLM deployment at scale.

Discover how to avoid LLM security risks with Fiddler AI. Our observability and guardrails tools detect risks and set alerts for quick jailbreak responses.

Learn how to evaluate AI agents with key steps and metrics using Fiddler to boost performance, ensure reliability, and support continuous improvement.

Discover how to build RAG-based LLM applications for production with Fiddler, and learn how to safeguard, monitor, and optimize LLM performance.

Discover how AI predictive analytics helps forecast model behavior, optimize outcomes, and improve decision-making with Fiddler AI Observability.

Learn best practices, importance, and benefits of managing multi-agent LLM systems in enterprises. Discover how Fiddler can assist with seamless management.

Learn how to build AI agents with this step-by-step guide for enterprises—covering frameworks, benefits, use cases, and Fiddler’s AI monitoring support.